Recommender systems: An introduction

Recommender Systems: An Introduction

Volumn 9780521493369, Issue , 2010, Pages 1-335

  Jannach, Dietmar ^a   Zanker, Markus ^b   Felfernig, Alexander ^c   Friedrich, Gerhard ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b UNIVERSITY OF KLAGENFURT   (Austria)

^c GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COLLABORATIVE FILTERING; CONSUMER BEHAVIOR; KNOWLEDGE BASED SYSTEMS;

ALGORITHMIC APPROACH; CONSUMER BUYING; CONTENT BASED FILTERING; EMERGING TOPICS; INFORMATION OVERLOADS; KNOWLEDGE-BASED APPROACH; LEISURE TIME; STATE OF THE ART;

RECOMMENDER SYSTEMS;

EID: 84923938582     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511763113     Document Type: Book

References (420)

1. G. D. Abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton, Cyberguide: A mobile context-aware tour guide, Wireless Networks 3 (1997), no. 5, 421-433.
2. G. Adomavicius and Y. O. Kwon, New recommendation techniques for multicriteria rating systems, Intelligent Systems, IEEE 22 (2007), no. 3, 48-55.
3. G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems 23 (2005), no. 1, 103-145.
4. G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering 17 (2005), no. 6, 734-749.
5. R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), Morgan Kaufmann, 1994, pp. 487-499.
6. J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang, Topic detection and tracking pilot study final report, Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 1998, pp. 194-218.
7. S. S. Anand and B. Mobasher, Intelligent techniques for web personalization, Lecture Notes in Computer Science, vol. 3169, Springer, Acapulco, Mexico, 2005, pp. 1-36.
8. S. S. Anand and B. Mobasher, Contextual recommendation, From Web to Social Web: Discovering and Deploying User and Content Profiles: Workshop on Web Mining (Berlin, Germany), Springer, 2007, pp. 142-160.
9. L. Ardissono, A. Felfernig, G. Friedrich, A. Goy, D. Jannach, G. Petrone, R. Schäfer, and M. Zanker, A Framework for the Development of Personalized, Distributed Web-Based Configuration Systems, AI Magazine 24 (2003), no. 3, 93-110.
10. L. Ardissono, A. Goy, G. Petrone, and M. Segnan, A multi-agent infrastructure for developing personalized web-based systems, ACMTransactions on Internet Technology 5 (2005), no. 1, 47-69.
11. L. Ardissono, A. Goy, G. Petrone, M. Segnan, and P. Torasso, Intrigue: Personalized recommendation of tourist attractions for desktop and handset devices, Applied Artificial Intelligence 17 (2003), no. 8-9, 687-714.
12. E. Aronson, T. Wilson, and A. Akert, Social psychology, 6th ed., Pearson Prentice Hall, 2007.
13. R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval, Addison-Wesley, 1999.
14. M. Balabanović and Y. Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM 40 (1997), no. 3, 66-72.
15. P. Basile, D. Gendarmi, F. Lanubile, and G. Semeraro, Recommending smart tags in a social bookmarking system, Proceedings of the International Workshop Bridging the Gap between SemanticWeb andWeb 2.0 at ESWC 2007 (Innsbruck, Austria), 2007, pp. 22-29.
16. C. Basu, H. Hirsh, and W. Cohen, Recommendation as classification: using social and content-based information in recommendation, in Proceedings of the 15th National Conference on Artificial Intelligence (AAAI’98) (Madison, WI), American Association for Artificial Intelligence, 1998, pp. 714-720.
17. G. Beenen, K. Ling, X. Wang, K. Chang, D. Frankowski, P. Resnick, and R. Kraut, Using social psychology to motivate contributions to online communities, Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work (CSCW ’04) (Chicago), 2004, pp. 212-221.
18. T. Beer, M. Fuchs, W. Höpken, J. Rasinger, and H. Werthner, Caips: A context-aware information push service in tourism, Proceedings of the 14th International Conference on Information and Communication Technologies in Tourism 2007 (ENTER) (Ljubljana, Slovenia), Springer, January 2007, pp. 129-140.
19. F. Belanger, A conjoint analysis of online consumer satisfaction, Journal of Electronic Commerce Research 6 (2005), 95-111.
20. R. Bell, J. Bennett, Y. Koren, and C. Volinsky, The million dollar programming prize, IEEE Spectrum (2009), 28-33.
21. R. M. Bell, Y. Koren, and C. Volinsky, The BellKor solution to the Netflix Prize, Tech.Report http://www.netflixprize.com/assets/ProgressPrize2007KorBell.pdf, AT&TLabs Research, 2007.
22. A. Bellogín, I. Cantador, P. Castells, and A. Ortigosa, Discerning relevant model features in a content-based collaborative recommender system, Preference Learning (J. Fürnkranz and E. Hüllermeier, eds.), Springer, 2010.
23. L. D. Bergman, A. Tuzhilin, R. Burke, A. Felfernig, and L. Schmidt-Thieme (eds.), Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys ’09), New York, 2009.
24. S. Berkovsky, Y. Eytani, T. Kuflik, and F. Ricci, Enhancing privacy and preserving accuracy of a distributed collaborative filtering, Proceedings of the 2007ACMConference on Recommender Systems (RecSys ’07) (Minneapolis), ACM, 2007, pp. 9-16.
25. J. Bettman, M. Luce, and J. Payne, Constructive consumer choice processes, Journal of Consumer Research 25 (1998), no. 3, 187-217.
26. R. Bhaumik, R. D. Burke, and B. Mobasher, Crawling attacks against web-based recommender systems, Proceedings of the 2007 International Conference on Data Mining (DMIN ’07) (Las Vegas) (Robert Stahlbock, Sven F. Crone, and Stefan Lessmann, eds.), June 2007, pp. 183-189.
27. R. Bhaumik, C. Williams, B. Mobasher, and R. Burke, Securing collaborative filtering against malicious attacks through anomaly detection, Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization (ITWP ’06) (Boston), July 2006.
28. D. Billsus and M. Pazzani, User modeling for adaptive news access, User Modeling and User-Adapted Interaction: The Journal of Personalization Research 10 (2000), no. 2-3, 147-180.
29. D. Billsus and M. J. Pazzani, Learning collaborative information filters, Proceedings of the 15th International Conference on Machine Learning (ICML’98), Morgan Kaufmann, San Francisco, 1998, pp. 46-54.
30. D. Billsus and M. J. Pazzani, A personal news agent that talks, learns and explains, Proceedings of the 3rd Annual Conference on Autonomous Agents (AGENTS’99) (Seattle), ACM, 1999, pp. 268-275.
31. D. Billsus and M. J. Pazzani, Adaptive news access, The Adaptive Web (Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, eds.), Lecture Notes in Computer Science, vol. 4321, Springer, 2007, pp. 550-570.
32. D. Billsus, M. J. Pazzani, and J. Chen, A learning agent for wireless news access, Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI ’00) (New Orleans), ACM, 2000, pp. 33-36.
33. F. Bohnert, I. Zukerman, S. Berkovsky, T. Baldwin, and L. Sonenberg, Using interest and transition models to predict visitor locations in museums, AI Communications 21 (2008), no. 2-3, 195-202.
34. C. Bomhardt, NewsRec, a SVM-driven Personal Recommendation System for News Websites, Proceedings of the 2004 IEEE/WIC/ACMInternational Conference onWeb Intelligence (WI ’04) (Washington, DC), IEEE Computer Society, 2004, pp. 545-548.
35. S. Botti and S. Iyengar, The psychological pleasure and pain of choosing: When people prefer choosing at the cost of subsequent outcome satisfaction, Journal of Personality and Social Psychology 87 (2004), no. 3, 312-326.
36. A. Bouza, G. Reif, A. Bernstein, and H. Gall, Semtree: Ontology-based decision tree algorithm for recommender systems, International SemanticWebConference (Posters and Demos), CEUR Workshop Proceedings, vol. 401, CEUR-WS.org, 2008.
37. J. S. Breese, D. Heckerman, and C. M. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (Madison, WI) (Gregory F. Cooper and Serafín Moral, eds.), Morgan Kaufmann, 1998, pp. 43-52.
38. W. F. Brewer, C. A. Chinn, and A. Samarapungavan, Explanation in scientists and children, Minds and Machines 8 (1998), no. 1, 119-136.
39. D. Bridge, M. Göker, L. McGinty, and B. Smyth, Case-based recommender systems, Knowledge Engineering Review 20 (2005), no. 3, 315-320.
40. S. Brin and L. Page, The anatomy of a large-scale hypertextual web search engine, Computer Networks and ISDN Systems 30 (1998), no. 1-7, 107-117.
41. B. G. Buchanan and E. H. Shortliffe, Rule-based expert systems: TheMycin experiments of the Stanford Heuristic Programming Project (the Addison-Wesley series in artificial intelligence), Addison-Wesley Longman, Boston, 1984.
42. C. Buckley, G. Salton, and J. Allan, The effect of adding relevance information in a relevance feedback environment, Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’94) (Dublin), Springer, 1994, pp. 292-300.
43. W. L. Buntine, M. Grobelnik, D. Mladenic, and J. Shawe-Taylor, European conference on machine learning and principles and practice of knowledge discovery in databases, ECML/PKDD (1) (Bled, Slovenia), Lecture Notes in Computer Science, vol. 5781, Springer, September 2009.
44. R. Burke, Knowledge-based recommender systems, Encyclopedia of Library and Information Science 69 (2000), no. 32, 180-200.
45. R. Burke, Interactive critiquing for catalog navigation in e-commerce, Artificial Intelligence Review 18 (2002a), no. 3-4, 245-267.
46. R. Burke, The wasabi personal shopper: A case-based recommender system, Proceedings of the 16thNational Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence (AAAI’99/IAAI’99) (Orlando, FL), AAAI Press, 1999, pp. 844-849.
47. R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction 12 (2002b), no. 4, 331-370.
48. R. Burke, P. Brusilovsky and A. Kobsa and W. Nejdl, Hybrid web recommender systems, The Adaptive Web: Methods and Strategies of Web Personalization, Springer, Heidelberg, Germany, 2007, pp. 377-408.
49. R. Burke, K. Hammond, and B. Young, The findme approach to assisted browsing, IEEE Expert 4 (1997), no. 12, 32-40.
50. R. Burke, K. J. Hammond, and B. C. Young, Knowledge-based navigation of complex information spaces, Proceedings of the 13th National Conference on Artificial Intelligence (AAAI ’96) (Portland, OR), AAAI Press, 1996, pp. 462-468.
51. R. Burke, B. Mobasher, and R. Bhaumik, Limited knowledge shilling attacks in collaborative filtering systems, in Proceedings of the 3rd IJCAI Workshop in Intelligent Techniques for Personalization (Edinburgh, Scotland), 2005, pp. 17-24.
52. A. Byde, H. Wan, and S. Cayzer, Personalized tag recommendations via tagging and content-based similarity metrics, Proceedings of the International Conference on Weblogs and Social Media (ICWSM ’07), poster session (Boulder, CO), 2007.
53. J. Canny, Collaborative filtering with privacy, Proceedings of the 2002 IEEE Symposium on Security and Privacy (SP ’02) (Washington, DC), IEEE Computer Society, 2002a, pp. 45-57.
54. J. Canny, Collaborative filtering with privacy via factor analysis, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’02) (Tampere, Finland), ACM, 2002b, pp. 238-245.
55. G. Carenini and J. Moore, Generating and evaluating evaluative arguments, Artificial Intelligence 170 (2006), 925-952.
56. G. Carenini and J. D. Moore, An empirical study of the influence of user tailoring on evaluative argument effectiveness, Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI ’01) (Seattle) (Bernhard Nebel, ed.), Morgan Kaufmann, August 2001, pp. 1307-1312.
57. G. Carenini and R. Sharma, Exploring more realistic evaluation measures for collaborative filtering, Proceedings of the 19th National Conference on Artificial Intelligence (AAAI) (San Jose, CA), AAAI Press, 2004, pp. 749-754.
58. O. Celma and P. Herrera, A new approach to evaluating novel recommendations, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), ACM Press, 2008, pp. 179-186.
59. S. Chakrabarti, Mining the web: Discovering knowledge from hypertext data, Science and Technology Books, 2002.
60. P. Chau, M. Cole, A. Massey, M. Montoya-Weiss, and R. O’Keefe, Cultural differences in the online behavior of consumers, Communications of the ACM 10 (2002), no. 45, 138-143.
61. S.H. S. Chee, J. Han, and K. Wang, Rectree: An efficient collaborative filtering method, Proceedings of the 3rd InternationalConference on DataWarehousing and Knowledge Discovery (Munich), 2001, pp. 141-151.
62. K. Chellapilla and P. Y. Simard, Using machine learning to break visual human interaction proofs (HIPS), Proceedings of the 18thAnnualConference on Neural Information Processing Systems (NIPS ’04), 2004, pp. 265-272.
63. L. Chen and P. Pu, Trust building in recommender agents, 1st International Workshop onWeb Personalisation, Recommender Systems and Intelligent User Interfaces (WPRSIUI ’05) (Reading, UK), 2005, pp. 135-145.
64. L. Chen and P. Pu, A cross-cultural user evaluation of product recommender interfaces, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), ACM, 2008, pp. 75-82.
65. Y.-H. Chen and E. I. George, A Bayesianmodel for collaborative filtering, in Proceedings of Uncertainty 99: The Seventh InternationalWorkshop on Artificial Intelligence and Statistics (Fort Lauderdale, FL), January 1999.
66. J. A. Chevalier and D. Mayzlin, The effect of word of mouth on sales: Online book reviews, Journal of Marketing Research 43 (2006), no. 9, 345-354.
67. K. Cheverst, K. Mitchel, and N. Davies, Exploring context-aware information push, Personal and Ubiquitous Computing 6 (2002a), no. 4, 276-281.
68. K. Cheverst, K. Mitchel, and N. Davies, The role of adaptive hypermedia in an context-aware tourist guide, Communications of the ACM 45 (2002b), no. 5, 47-51.
69. D. Chickering, D. Heckerman, and C. Meek, A Bayesian approach to learning Bayesian networks with local structure, Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence (UAI ’97) (San Francisco), Morgan Kaufmann, 1997, pp. 80-89.
70. P.-A. Chirita, W. Nejdl, and C. Zamfir, Preventing shilling attacks in online recommender systems, Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management (WIDM ’05) (Bremen, Germany), ACM, 2005, pp. 67-74.
71. Y. H. Cho, C. Y. Kim, and D.-H. Kim, Personalized image recommendation in the mobile internet, Proceedings of 8th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’04) (Auckland, New Zealand), Lecture Notes in Computer Science, vol. 3157, Springer, 2004, pp. 963-964.
72. B. Choi, I. Lee, J. Kim, and Y. Jeon, A qualitative cross-national study of cultural influences on mobile data, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’05) (Portland, OR), ACM, 2005, pp. 661-670.
73. M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, Combining content-based and collaborative filters in an online newspaper, Proceedings of the ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation (Berkeley, CA), 1999.
74. W. Cohen, Learning rules that classify e-mail, Proceedings of the AAAI Symposium on Machine Learning in Information Access (Stanford, CA) (Marti Hearst and Haym Hirsh, eds.), 1996, pp. 18-25.
75. W. W. Cohen, Fast effective rule induction, Proceedings of the 12th International Conference on Machine Learning (ICML ’95) (Tahoe City, CA) (Armand Prieditis and Stuart Russell, eds.), Morgan Kaufmann, July 1995, pp. 115-123.
76. L. Console, D. T. Dupre, and P. Torasso, On the relationship between abduction and deduction, Journal of Logic and Computation 1 (1991), no. 5, 661-690.
77. D. Cosley, S. Lam, I. Albert, J. Konstan, and J. Riedl, Is seeing believing? How recommender system interfaces affect users’ opinions, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’03) (Fort Lauderdale, FL), 2003, pp. 585-592.
78. P. Cotter and B. Smyth, PTV: Intelligent personalised tv guides, Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI Press/MIT Press, 2000, pp. 957-964.
79. J. W. Creswell, Research design: Qualitative, quantitative and mixed methods approaches, 3rd ed., SAGE Publications, 2009.
80. A. S. Das, M. Datar, A. Garg, and S. Rajaram, Google news personalization: scalable online collaborative filtering, Proceedings of the 16th International Conference on World Wide Web (WWW ’07) (New York), ACM Press, 2007, pp. 271-280.
81. S. Deerwester, S. T. Dumais, G.W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American Society for Information Science 41 (1990), 391-407.
82. M. de Gemmis, P. Lops, G. Semeraro, and P. Basile, Integrating tags in a semantic content-based recommender, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08), ACM, Lausanne, Switzerland, 2008, pp. 163-170.
83. C. Dellarocas, Strategic manipulation of internet opinion forums: Implications for consumers and firms, Management Science 52 (2006), no. 10, 1577-1593.
84. F. H. del Olmo and E. Gaudioso, Evaluation of recommender systems: A new approach, Expert Systems with Applications 35 (2008), no. 3, 790-804.
85. A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B 39 (1977), no. 1, 1-38.
86. J. Demšar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006), 1-30.
87. M. Deshpande and G. Karypis, Item-based top-n recommendation algorithms, ACM Transactions on Information Systems 22 (2004), no. 1, 143-177.
88. M. B. Dias, D. Locher, M. Li, W. El-Deredy, and P. J. G. Lisboa, The value of personalised recommender systems to e-business: a case study, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), 2008, pp. 291-294.
89. A. Dix, T. Rodden, N. Davies, J. Trevor, A. Friday, and K. Palfreyman, Exploiting space and location as a design framework for interactivemobile systems, ACMTransactions on Computer-Human Interaction 7 (2000), no. 3, 285-321.
90. P. Domingos and M. J. Pazzani, Beyond independence: Conditions for the optimality of the simple Bayesian classifier, Proceedings of the 13th International Conference on Machine Learning (ICML ’96) (Bari, Italy), 1996, pp. 105-112.
91. P. Domingos and M. J. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning 29 (1997), no. 2-3, 103-130.
92. P. Duttweiler, The internal control index: A newly developed measure of locus of control, Educational and Psychological Measurement 44 (1984), 209-221.
93. B. Fasolo, G. McClelland, and P. Todd, Escaping the tyranny of choice: When fewer attributes make choice easier, Marketing Theory 7 (2007), no. 1, 13-26.
94. A. Felfernig and R. Burke, Constraint-based recommender systems: technologies and research issues, Proceedings of the 10th International Conference on Electronic Commerce (ICEC ’08) (Innsbruck, Austria), ACM, 2008, pp. 1-10.
95. A. Felfernig, G. Friedrich, B. Gula, M. Hitz, T. Kruggel, R. Melcher, D. Riepan, S. Strauss, E. Teppan, and O. Vitouch, Persuasive recommendation: Exploring serial position effects in knowledge-based recommender systems, Proceedings of the 2nd International Conference of Persuasive Technology (Persuasive ’07) (Stanford, California), vol. 4744, Springer, 2007a, pp. 283-294.
96. A. Felfernig, G. Friedrich, M. Schubert, M. Mandl, M. Mairitsch, and E. Teppan. PlausibleRepairs for Inconsistent Requirements, Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI ’09), Pasadena, California, USA, pp. 791-796, 2009.
97. A. Felfernig, G. Friedrich, D. Jannach, and M. Stumptner, Consistency-based diagnosis of configuration knowledge bases, Artificial Intelligence 152 (2004), no. 2, 213-234.
98. A. Felfernig, B. Gula, G. Leitner, M. Maier, R. Melcher, S. Schippel, and E. Teppan, A dominance model for the calculation of decoy products in recommendation environments, Proceedings of the AISB Symposium on Persuasive Technologies (Aberdeen, Scotland), vol. 3, University of Aberdeen, 2008a, pp. 43-50.
99. A. Felfernig, B. Gula, and E. Teppan, Knowledge-based recommender technologies for marketing and sales, International Journal of Pattern Recognition and Artificial Intelligence 21 (2006), no. 2, 1-22.
100. A. Felfernig, K. Isak, K. Szabo, and P. Zachar, The VITA financial services sales support environment, Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI ’07), AAAI, 2007b, pp. 1692-1699.
101. A. Felfernig, M. Mairitsch, M. Mandl, M. Schubert, and E. Teppan, Utility-based repair of inconsistent requirements, 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE (Tainan, Taiwan), 2009, pp. 162-171.
102. A. Felfernig and K. Shchekotykhin, Debugging user interface descriptions of knowledge-based recommender applications, Proceedings of the 11th International Conference on Intelligent User Interfaces (IUI ’06) (Sydney, Australia), ACM Press, 2006, pp. 234-241.
103. A. Felfernig and E. Teppan, The asymmetric dominance effect and its role in e-tourism recommender applications, Proceedings of Wirtschaftsinformatik 2009, Austrian Computer Society, 2009, pp. 791-800.
104. A. Felfernig, E. Teppan, G. Leitner, R. Melcher, B. Gula, and M. Maier, Persuasion in knowledge-based recommendation, Proceedings of the 2nd International Conference on Persuasive Technologies (Persuasive ’08) (Oulu, Finland), vol. 5033, Springer, 2008c, pp. 71-82.
105. A. Felfernig, G. Friedrich, D. Jannach, and M. Zanker, An environment for the development of knowledge-based recommender applications, International Journal of Electronic Commerce 11 (2006), no. 2, 11-34.
106. A. Felfernig, G. Friedrich, D. Jannach, and M. Zanker, An integrated environment for the development of knowledge-based recommender applications, International Journal of Electronic Commerce 11 (2006-07), no. 2, 11-34.
107. A. Felfernig and B. Gula, An empirical study on consumer behavior in the interaction with knowledge-based recommender applications, Proceedings of the 8th IEEE International Conference on E-Commerce Technology (CEC ’06)/3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE ’06) (Palo Alto, CA), 2006, p. 37.
108. C. S. Firan, W. Nejdl, and R. Paiu, The benefit of using tag-based profiles, Proceedings of the 2007 Latin American Web Conference (LA-WEB ’07) (Washington, DC), IEEE Computer Society, 2007, pp. 32-41.
109. D. M. Fleder and K. Hosanagar, Recommender systems and their impact on sales diversity, Proceedings of the 8th ACM Conference on Electronic Commerce (EC ’07) (San Diego, California, USA), 2007, pp. 192-199.
110. B. J. Fogg, Persuasive technologies, Communications of the ACM 42 (1999), no. 5, 26-29.
111. B. J. Fogg, Persuasive technology-using computers to change what we think and do, Morgan Kaufmann, 2003.
112. P. W. Foltz and S. T. Dumais, Personalized information delivery: an analysis of information filtering methods, Communications of the ACM 35 (1992), no. 12, 51-60.
113. J. H. Friedman, On bias, variance, 0/1-loss, and the curse-of-dimensionality, Data Mining and Knowledge Discovery 1 (1997), no. 1, 55-77.
114. G. Friedrich, Elimination of spurious explanations, Proceedings of the 16th European Conference on Artificial Intelligence (ECAI ’04), including Prestigious Applicants of Intelligent Systems (PAIS ’04) (Valencia, Spain) (Ramon López de Mántaras and Lorenza Saitta, eds.), IOS Press, August 2004, pp. 813-817.
115. X. Fu, J. Budzik, and K. J. Hammond, Mining navigation history for recommendation, Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI ’00) (New Orleans), ACM, 2000, pp. 106-112.
116. A. Garcia-Crespo, J. Chamizo, I. Rivera, M. Mencke, R. Colomo-Palacios, and J. M. Gómez-Berbís, Personalizing recommendations for tourists, Telematics and Informatics 26 (2009), no. 3, 306-315.
117. S. Garcìa and F. Herrera, An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons, Journal of Machine Learning Research 9 (2008), 2677-2694.
118. T. George and S. Merugu, A scalable collaborative filtering framework based on coclustering, Proceedings of the 5th IEEE International Conference on Data Mining (ICDM ’05) (Washington, DC), IEEE Computer Society, 2005, pp. 625-628.
119. F. Gershberg and A. Shimamura, Serial position effects in implicit and explicit tests of memory, Journal of Experimental Psychology: Learning, Memory, and Cognition 20 (1994), no. 6, 1370-1378.
120. G. Gigerenzer, Bauchentscheidungen, Bertelsmann Verlag, March 2007.
121. J. Golbeck, Semantic web interaction through trust network recommender systems, End User Semantic Web Interaction Workshop at the 4th International Semantic Web Conference, Galway, Ireland, 2005.
122. J. Golbeck, Generating predictive movie recommendations from trust in social networks, Proceedings of the 4th International Conference on Trust Management (iTrust ’06) (Pisa, Italy), May 2006, pp. 93-104.
123. J. Golbeck and J. Hendler, Inferring binary trust relationships in web-based social networks, ACM Transactions Internet Technology 6 (2006), no. 4, 497-529.
124. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM 35 (1992), no. 12, 61-70.
125. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, Eigentaste: A constant time collaborative filtering algorithm, Information Retrieval 4 (2001), no. 2, 133-151.
126. N. Golovin and E. Rahm, Reinforcement learning architecture for web recommendations, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC ’04) (Las Vegas), vol. 2, 2004, pp. 398-402.
127. G. Golub and W. Kahan, Calculating the singular values and pseudo-inverse of a matrix, Journal of the Society for Industrial and Applied Mathematics, Series B: Numerical Analysis 2 (1965), no. 2, 205-224.
128. G. Gonzalez, B. Lopez, and J. D. L. Rosa, The emotional factor: An innovative approach to user modelling for recommender systems, Proceedings of AH2002 Workshop on Recommendation and Personalization in e-Commerce (Malaga, Spain), 2002, pp. 90-99.
129. G. González, J. L. de laRosa, and M. Montaner, Embedding emotional context in recommender systems, Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference (Key West, FL), AAAI Press, 2007, pp. 454-459.
130. S. Grabner-Kräuter and E. A. Kaluscha, Empirical research in on-line trust: a review and critical assessment, International Journal ofHuman-Computer Studies 58 (2003), no. 6, 783-812.
131. D. Grether and C. Plott Economic theory of choice and the preference reversal phenomenon, American Economic Review 69 (1979), no. 4, 623-638.
132. U. Gretzel and D. Fesenmaier, Persuasion in recommender systems, International Journal of Electronic Commerce 11 (2006), no. 2, 81-100.
133. I. Grigorik, SVD recommendation system in ruby, web blog, 01 2007, http://www.igvita.com/2007/01/15/svd-recommendation-system-in-ruby/[accessedMarch 2009].
134. R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, Propagation of trust and distrust, Proceedings of the 13th International Conference on World Wide Web (WWW ’04) (New York), ACM, 2004, pp. 403-412.
135. I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman, Personalized recommendation of social software items based on social relations, Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys ’09), New York, 2009, pp. 53-60.
136. G. Häubl and K. Murray, Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents, Journal of Consumer Psychology 13 (2003), 75-91.
137. G. Häubl and K. Murray, Double agents, MIT Sloan Management Review 47 (2006), no. 3, 7-13.
138. G. Häubl and V. Trifts, Consumer decision making in online shopping environments: The effects of interactive decision aids, Marketing Science 19 (2000), no. 1, 4-21.
139. K. Hegelich and D. Jannach, Effectiveness of different recommender algorithms in the mobile internet: A case study, Proceedings of the 7th Workshop on Intelligent Techniques forWeb Personalization and Recommender Systems (ITWP) at IJCAI ’09 (Pasadena, CA), 2009, pp. 41-50.
140. J. Herlocker, J. A. Konstan, and J. Riedl, An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Information Retrieval 5 (2002), no. 4, 287-310.
141. J. L. Herlocker, J. A. Konstan, et al., An Algorithmic Framework for Performing Collaborative Filtering, Proceedings of the 22nd Annual International ACM SIGIR Conference, ACM Press, 1999, pp. 230-237.
142. J. L. Herlocker, J. A. Konstan, and J. Riedl, Explaining collaborative filtering recommendations, Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW ’00) (Philadelphia), ACM, 2000, pp. 241-250.
143. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS) 22 (2004), no. 1, 5-53.
144. A. Herrmann, M. Heitmann, and B. Polak, The power of defaults, Absatzwirtschaft 6 (2007), 46-47.
145. C. Hess, K. Stein, and C. Schlieder, Trust-enhanced visibility for personalized document recommendations, Proceedings of the 2006 ACMSymposium on Applied Computing (SAC ’06) (Dijon, France) (Hisham Haddad, ed.), ACM, 2006, pp. 1865-1869.
146. A. R. Hevner, S. T. March, J. Park, and S. Ram, Design science in information systems research, MIS Quarterly 28 (2004), no. 1, 75-105.
147. W. Hill, L. Stead, M. Rosenstein, and G. Furnas, Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’95) (Denver), 1995, pp. 194-201.
148. W. Höpken, M. Fuchs, M. Zanker, T. Beer, A. Eybl, S. Flores, S. Gordea, M. Jessenitschnig, T. Kerner, D. Linke, J. Rasinger, and M. Schnabl, etPlanner: An IT framework for comprehensive and integrative travel guidance, Proceedings of the 13th International Conference on Information Technology and Travel and Tourism (ENTER) (Lausanne, Switzerland), 2006, pp. 125-134.
149. T. Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’99) (Berkeley, CA), 1999, pp. 50-57.
150. T. Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems 22 (2004), no. 1, 89-115.
151. T. Hofmann and J. Puzicha, Latent class models for collaborative filtering, Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI ’99) (San Francisco), 1999, pp. 688-693.
152. W. Höpken, M. Scheuringer, D. Linke, and M. Fuchs, Context-based adaptation of ubiquitous web applications in tourism, Information and Communication Technologies in Tourism (ENTER) (Innsbruck, Austria), 2008, pp. 533-544.
153. A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme, Information retrieval in folksonomies, Proceedings of the European Semantic Web Conference 2006 (Budva, Montenegro), Lecture Notes in Computer Science, vol. 4011, Springer, 2007, pp. 411-426.
154. E. M. Housman and E. D. Kaskela, State of the art in selective dissemination of information, IEEE Transactions on Engineering Writing and Speech 13 (1970), no. 2, 78-83.
155. C.-N. Hsu, H.-H. Chung, and H.-S. Huang, Mining skewed and sparse transaction data for personalized shopping recommendation, Machine Learning 57 (2004), no. 1-2, 35-59.
156. Y.-C. Huang, J. Y. jen Hsu, and D. K.-C. Wu, Tag-based user profiling for social media recommendation, Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP) at AAAI ’08 (Chicago), AAAI Press, 2008, pp. 49-55.
157. Z. Huang, H. Chen, and D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems 22 (2004), no. 1, 116-142.
158. J. Huber, W. Payne, and C. Puto, Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis, Journal of Consumer Research 9 (1982), 90-98.
159. C. Huffman and B. Kahn, Variety for sale: Mass customization or mass confusion, Journal of Retailing 74 (1998), no. 4, 491-513.
160. N. J. Hurley, M. P. O’Mahony, and G. C. M. Silvestre, Attacking recommender systems: A cost-benefit analysis, IEEE Intelligent Systems 22 (2007), no. 3, 64-68.
161. J. Hutchinson, Is more choice always desirable? Evidence and arguments from leks, food, selection, and environmental enrichment, Biological Reviews 80 (2005), 73-92.
162. I. Im and A. Hars, Does a one-size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes, ACM Transactions on Information Systems 26 (2007), no. 1, 4.
163. S. Iyengar, R. Wells, and B. Schwartz, Doing better but feeling worse: Looking for the best job undermines satisfaction, Psychological Science 17 (2006), no. 2, 143-150.
164. D. Jannach, Advisor suite-a knowledge-based sales advisory system, Proceedings of European Conference on Artificial Intelligence (Valencia, Spain) (R. Lopez de Mantaras and L. Saitta, eds.), IOS Press, 2004, pp. 720-724.
165. D. Jannach, Finding preferred query relaxations in content-based recommenders, Proceedings of IEEE Intelligent Systems Conference (IS ’2006) (Westminster, UK), IEEE Press, 2006a, pp. 355-360.
166. D. Jannach, Techniques for fast query relaxation in content-based recommender systems, Proceedings of the 29th German Conference on Artificial Intelligence (KI ’06) (Bremen, Germany) (C. Freksa, M. Kohlhase, and K. Schill, eds.), Lecture Notes in Artificial Intelligence, vol. 4314, Springer, 2006b, pp. 49-63.
167. D. Jannach and K. Hegelich, A case study on the effectiveness of recommendations in the mobile internet, Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys ’09) (New York), 2009, pp. 41-50.
168. D. Jannach, K. Shchekotykhin, and G. Friedrich, Automated ontology instantiation from tabular web sources-the Allright system, Web Semantics: Science, Services and Agents on the World Wide Web 7 (2009a), no. 3, 136-153.
169. D. Jannach, M. Zanker, and M. Fuchs, Constraint-based recommendation in tourism: A multi-perspective case study, Information Technology and Tourism 11 (2009b), no. 2, 139-156.
170. D. Jannach, M. Zanker, M. Jessenitschnig, and O. Seidler, Developing a Conversational Travel AdvisorwithADVISOR SUITE, Information and Communication Technologies in Tourism (ENTER ’07) (Ljubljana, Slovenia) (Marianna Sigala, Luisa Mich, and Jamie Murphy, eds.), Springer, 2007, pp. 43-52.
171. S. Jarvenpaa and P. Todd, Consumer reactions to electronic shopping on the world wide web, International Journal of Electronic Commerce 1 (1996), no. 2, 59-88.
172. S. Jarvenpaa, N. Tractinsky, and M. Vitale, Consumer trust in an internet store, Information Technology and Management 1 (2000), no. 1-2, 45-71.
173. R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme, Tag recommendations in folksonomies, Knowledge Discovery in Databases: PKDD 2007 (Warsaw), Lecture Notes in Computer Science, vol. 4702, Springer, 2007, pp. 506-514.
174. Z. Jiang, W., and I. Benbasat, Multimedia-based interactive advising technology for online consumer decision support, Communications of the ACM 48 (2005), no. 9, 92-98.
175. R. Jin, L. Si, and C. Zhai, A study of mixture models for collaborative filtering, Information Retrieval 9 (2006), no. 3, 357-382.
176. T. Joachims, Text categorization with support vector machines: learning with many relevant features, Proceedings of the 10th European Conference on Machine Learning (ECML-98) (Chemnitz, Germany) (Claire Nédellec and Céline Rouveirol, eds.), no. 1398, Springer Verlag, Heidelberg, Germany, 1998, pp. 137-142.
177. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28th Annual International ACM SIGIR Conference (SIGIR ’05) (Salvador, Brazil), ACM, 2005, pp. 154-161.
178. A. Jøsang, S. Marsh, and S. Pope, Exploring different types of trust propagation, Proceedings of the 4th International Conference on TrustManagement (iTrust ’06) (Pisa, Italy), Lecture Notes in Computer Science, vol. 3986, Springer, 2006, pp. 179-192.
179. U. Junker, QUICKXPLAIN: Preferred explanations and relaxations for overconstrained problems, Proceedings of the 19th National Conference on Artificial Intelligence (AAAI ’04) (San Jose, CA), AAAI, 2004, pp. 167-172.
180. K. Kabassi, Personalizing recommendations for tourists, Telematics and Informatics 27 (2010), no. 1, 51-66.
181. S. Kaplan, H. Reneau, and S. Whitecotton, The effects of predictive ability information, locus of control, and decision maker involvement on decision aid reliance, Journal of Behavioral Decision Making 14 (2001), 35-50.
182. J.W. Kim, B. H. Lee, M. J. Shaw, H.-L. Chang, and M. Nelson, Application of decisiontree induction techniques to personalized advertisements on internet storefronts, International Journal of Electronic Commerce 5 (2001), no. 3, 45-62.
183. J. Koenemann and N. J. Belkin, A case for interaction: a study of interactive information retrieval behavior and effectiveness, Proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI ’96) (Vancouver, BC), ACM, 1996, pp. 205-212.
184. J. Konstan, B. Miller, D. Maltz, J. L. Gordon, and J. Riedl, Grouplens: applying collaborative filtering to usenet news, Communications of the ACM 40 (1997), no. 3, 77-87.
185. I. Koprinska, J. Poon, J. Clark, and J. Chan, Learning to classify e-mail, Information Sciences 177 (2007), no. 10, 2167-2187.
186. Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer 42 (2009), no. 8, 30-37.
187. R. Kramer, M. Modsching, and K. ten Hagen, A city guide agent creating and adapting individual sightseeing tours based on field trial results, International Journal of Computational Intelligence Research 2 (2006), no. 2, 191-206.
188. R. Krestel and P. Fankhauser, Tag recommendation using probabilistic topic models, ECML/PKDD Discovery Challenge (DC ’09), Workshop at ECML/PKDD 2009) (Bled, Slovenia) (Folke Eisterlehner, Andreas Hotho, and Robert Jäschke, eds.), September 2009, pp. 131-141.
189. R. Krestel, P. Fankhauser, and W. Nejdl, Latent dirichlet allocation for tag recommendation, Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys ’09) (New York), 2009, pp. 61-68.
190. V. Krishnan, P. K. Narayanashetty, M. Nathan, R. T. Davies, and J. A. Konstan, Who predicts better?: Results from an online study comparing humans and an online recommender system, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), ACM, 2008, pp. 211-218.
191. J. Krösche, J. Baldzer, and S. Boll, MobiDENK-mobile multimedia in monument conservation, IEEE Multimedia 11 (2004), no. 2, 72-77.
192. A. Kruglanski, D. Webster, and A. Klem, Motivated resistance and openness to persuasion in the presence or absence of prior information, Journal of Personality and Social Psychology 65 (1993), no. 5, 861-876.
193. B. Krulwich and C. Burkey, The infofinder agent: Learning user interests through heuristic phrase extraction, IEEE Expert: Intelligent Systems and Their Applications 12 (1997), no. 5, 22-27.
194. S. K. Lam and J. Riedl, Shilling recommender systems for fun and profit, Proceedings of the 13th International Conference on World Wide Web (WWW ’04) (New York), ACM, 2004, pp. 393-402.
195. K. Lang, Newsweeder: learning to filter netnews, Proceedings of the 12th International Conference on Machine Learning (ICML ’95) (Tahoe City, CA), 1995, pp. 331-339.
196. N. Lathia, S. Hailes, and L. Capra, Private distributed collaborative filtering using estimated concordance measures, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis), ACM, 2007, pp. 1-8.
197. B.-H. Lee, H.-N. Kim, J.-G. Jung, and G.-S. Jo, Location-based service with context data for a restaurant recommendation, Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA ’06) (Krakow, Poland), 2006, pp. 430-438.
198. H.-H. Lee and W.-G. Teng, Incorporating multi-criteria ratings in recommendation systems, Proceedings of IEEE International Conference on Information Reuse and Integration (IRI ’07) (Las Vegas), 2007, pp. 273-278.
199. H. Lee, A. F. Smeaton, N. E. O’Connor, and B. Smyth, User evaluation of físchlár-news: An automatic broadcast news delivery system, ACM Transactions on Information Systems 24 (2006), no. 2, 145-189.
200. D. Lemire and A. Maclachlan, Slope one predictors for online rating-based collaborative filtering, Proceedings of the 5th SIAM International Conference on Data Mining (SDM ’05) (Newport Beach, CA), 2005, pp. 471-480.
201. I. Levin, S. Schneider, and G. Gaeth, All frames are not created equal: A typology and critical analysis of framing effects, Organizational Behavior and Human Decision Processes 76 (1998), 90-98.
202. D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka, Training algorithms for linear text classifiers, Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’96) (Zurich, Switzerland), ACM, 1996, pp. 298-306.
203. Q. Li, C. Wang, and G. Geng, Improving personalized services in mobile commerce by a novelmulticriteria rating approach, Proceedings of the 17th International Conference on World Wide Web (WWW ’08) (Beijing), 2008, pp. 1235-1236.
204. T. Li, M. Ogihara, and Q. Li, A comparative study on content-based music genre classification, Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’03) (Toronto), ACM, 2003, pp. 282-289.
205. W. Lin, Association rulemining for collaborative recommender systems, Master’s thesis, Worcester Polytechnic Institute, May 2000.
206. W. Lin, S. A. Alvarez, and C. Ruiz, Efficient adaptive-support association rule mining for recommender systems, Data Mining and Knowledge Discovery 6 (2002), no. 1, 83-105.
207. G. Linden, B. Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering, Internet Computing, IEEE 7 (2003), no. 1, 76-80.
208. C. Ling and C. Li, Data mining for direct marketing: Problems and solutions, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD ’98) (New York), 1998, pp. 73-79.
209. B. Logan, Music recommendation from song sets, Proceedings of 5th International Conference on Music Information Retrieval (ISMIR ’04) (Barcelona, Spain), 2004, pp. 425-428.
210. F. Lorenzi and F. Ricci, Case-based recommender systems: A unifying view, Intelligent Techniques for Web Personalisation, Lecture Notes in Artificial Intelligence, vol. 3169, Springer, 2005, pp. 89-113.
211. Z. Ma, G. Pant, and O. R. L. Sheng, Interest-based personalized search, ACM Transactions on Information Systems 25 (2007), no. 1, 5.
212. T. Mahmood and F. Ricci, Learning and adaptivity in interactive recommender systems, Proceedings of the 9th International Conference on Electronic Commerce (ICEC ’07), ACM, 2007, pp. 75-84.
213. V. Maidel, P. Shoval, B. Shapira, and M. Taieb-Maimon, Evaluation of an ontologycontent based filtering method for a personalized newspaper, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) Lawsanne, Switzerland) (Pearl Pu, Derek Bridge, Bamshad Mobasher, and Francisco Ricci, eds.), ACM, 2008, pp. 91-98.
214. O. Maimon and L. Rokach (eds.), The data mining and knowledge discovery handbook, Springer, 2005.
215. N. Mandel and E. Johnson, Constructing preferences online: canweb pages changewhat you want?, Unpublished manuscript (Wharton School, University of Pennsylvania), 1999.
216. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to information retrieval, Cambridge University Press, 2008.
217. B. M. Marlin and R. S. Zemel, Collaborative prediction and ranking with non-random missing data, Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys ’09) (New York), ACM, 2009, pp. 5-12.
218. P. Massa and P. Avesani, Trust-aware collaborative filtering for recommender systems, Springer, Lecture Notes in Computer Science, vol. 3290, 2004, pp. 492-508.
219. P. Massa and P. Avesani, Trust-aware recommender systems, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 17-24.
220. E. Maylor, Serial position effects in semantic memory: reconstructing the order of verses of hymns, Psychonomic Bulletin and Review 9 (2002), no. 4, 816-820.
221. D. Mayzlin, Promotional chat on the internet, Marketing Science 25 (2006), no. 2, 155-163.
222. A. McCallum and K. Nigam, A comparison of event models for naive bayes text classification, In AAAI-98Workshop on Learning for Text Categorization (Madison, WI), 1998.
223. K. McCarthy, J. Reilly, B. Smyth, and L. McGinty, Generating diverse compound critiques, Artificial Intelligence Review 24 (2005), no. 3-4, 339-357.
224. K. McCarthy, M. Salamó, L. Coyle, L. McGinty, B. Smyth, and P. Nixon, Group recommender systems: a critiquing based approach, Proceedings of the 11th International Conference on Intelligent User Interfaces (IUI ’06) (Sydney, Australia), ACM, 2006, pp. 267-269.
225. P. McCrae and P. Costa, The neo personality inventory: Using the five-factor model in counseling, Journal of Counseling and Development 69 (1991), 367-372.
226. D. McFadden, Rationality for economists?, Journal of Risk and Uncertainty 19 (1999), no. 1, 73-105.
227. L. McGinty and B. Smyth, On the role of diversity in conversational recommender systems, Proceedings of the International Conference on Case-Based Reasoning Research and Development (ICCBR ’03), 2003, pp. 276-290.
228. M. R. McLaughlin and J. L. Herlocker, A collaborative filtering algorithm and evaluation metric that accurately model the user experience, Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’04) (Sheffield, OK), ACM, 2004, pp. 329-336.
229. T. McNamara, Theories of priming II: Types of primes, Journal of Experimental Psychology: Learning, Memory, and Cognition 20 (1994), no. 3, 507-520.
230. S.M. McNee, J. Riedl, and J. A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, Extended Abstracts on Human Factors in Computing Systems (CHI ’06) (Montréal), ACM, 2006, pp. 1097-1101.
231. D. McSherry, Similarity and compromise, Proceedings of the 5th International Conference on Case-Based Reasoning (ICCBR ’03) (Trondheim, Norway), 2003a, pp. 291-305.
232. D. McSherry, Incremental relaxation of unsuccessful queries, Proceedings of the European Conference on Case-based Reasoning (P. Funk and P. A. Gonzalez Calero, eds.), Lecture Notes in Artificial Intelligence, vol. 3155, Springer, 2004, pp. 331-345.
233. D. McSherry, Similarity and compromise, Proceedings of the 5th International Conference on Case-Based Reasoning (ICCBR ’03) (Trondheim, Norway) (Kevin D. Ashley and Derek G. Bridge, eds.), LNCS, vol. 2689, Springer, June 2003b, pp. 291-305.
234. D. McSherry, Explanation in recommender systems, Artificial Intelligence Review24 (2005), no. 2, 179-197.
235. P. Melville, R. J. Mooney, and R. Nagarajan, Content-Boosted Collaborative Filtering for Improved Recommendations, Proceedings of the 18th National Conference on Artificial Intelligence (AAAI) (Edmonton, Alberta, Canada), 2002, pp. 187-192.
236. S. E. Middleton, N. R. Shadbolt, and D. C. De Roure, Ontological user profiling in recommender systems, ACM Transactions on Information Systems 22 (2004), no. 1, 54-88.
237. M. B. Miles and A. M. Huberman, Qualitative data analysis-an expanded sourcebook, 2nd ed., SAGE Publications, 1994.
238. B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl, MovieLens unplugged: experiences with an occasionally connected recommender system, Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI ’03) (Miami, FL), 2003, pp. 263-266.
239. B. N. Miller, J. A. Konstan, and J. Riedl, Pocketlens: Toward a personal recommender system, ACM Transactions on Information Systems 22 (2004), no. 3, 437-476.
240. G. Mishne, Autotag: a collaborative approach to automated tag assignment for weblog posts, Proceedings of the 15th International Conference onWorldWideWeb (WWW ’06) (Edinburgh, Scotland), ACM, 2006, pp. 953-954.
241. K. Miyahara and M. J. Pazzani, Collaborative filtering with the simple bayesian classifier, Pacific Rim International Conference on Artificial Intelligence (Melbourne, Australia), 2000, pp. 679-689.
242. D. Mladenic, Personal webwatcher: Design and implementation, Technical Report IJS-DP-7472 (Pittsburgh, PA), 1996.
243. D. Mladenic, Text-learning and related intelligent agents: A survey, IEEE Intelligent Systems 14 (1999), no. 4, 44-54.
244. B. Mobasher, R. Bhamik, and C. Williams, Effective attack models for shilling item-based collaborative filtering systems, Proceedings of the 2005 WebKDD Workshop, held in Conjuction with ACM SIGKDD ’05 (Chicago, IL), 2005, pp. 13-23.
245. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams, Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness, ACM Transactions on Internet Technology 7 (2007), no. 4, 23.
246. B. Mobasher, R. D. Burke, and J. J. Sandvig, Model-based collaborative filtering as a defense against profile injection attacks, Proceedings of National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference (Boston), AAAI Press, 2006, pp. 1388-1393.
247. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, Effective personalization based on association rule discovery from web usage data, Proceedings of the 3rd International Workshop onWeb Information and Data Management (WIDM ’01) (Atlanta), ACM, 2001, pp. 9-15.
248. B. Mobasher, X. Jin, and Y. Zhou, Semantically enhanced collaborative filtering on the web, Web Mining: FromWeb to SemanticWeb (B. Berendt et al., ed.), Lecture Notes in Computer Science, vol. 3209, Springer, 2004, pp. 57-76.
249. R. J. Mooney and L. Roy, Content-based book recommending using learning for text categorization, Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, pp. 195-204, June 2000.
250. J. Murphy, C. Hofacker, and R. Mizerski, Primacy and recency effects on clicking behavior, Journal of Computer-Mediated Communication 11 (2006), no. 2, 522-535.
251. T. Nakajima and I. Satoh, A software infrastructure for supporting spontaneous and personalized interaction in home computing environments, Personal Ubiquitous Computing 10 (2006), no. 6, 379-391.
252. T. Nathanson, E. Bitton, and K. Goldberg, Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering, Proceedings of the 2007ACMConference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 149-152.
253. A. Nauerz, B. König-Ries, and M. Welsch, Recommending background information and related content in web 2.0 portals, Proceedings Adaptive Hypermedia (Hannover, Germany), Lecture Notes in Computer Science, vol. 5149, Springer, 2008, pp. 366-69.
254. E. W. T. Ngai and A. Gunasekaran, A review for mobile commerce research and applications, Decision Support Systems 43 (2007), no. 1, 3-15.
255. Q. N. Nguyen and F. Ricci, Acquiring and revising preference in a critique-based mobile recommender system, IEEE Intelligent Systems 22 (2007), May/Jun, 22-29.
256. Q. N. Nguyen and F. Ricci, Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 81-88.
257. Q. N. Nguyen and F. Ricci, Long-term and session-specific user preferences in a mobile recommender system, Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI ’08) (Gran Canaria, Spain), 2008, pp. 381-384.
258. D. Nichols, Implicit rating and filtering, Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering (Budapest), ERCIM, 1998, pp. 31-36.
259. K. Nigam, A. K. McCallum, S. Thrun, and T. M. Mitchell, Learning to classify text from labeled and unlabeled documents, Proceedings of the 15th Conference of the American Association for Artificial Intelligence (AAAI ’98) (Madison, WI), 1998, pp. 792-799.
260. D. Nikovski and V. Kulev, Induction of compact decision trees for personalized recommendation, Proceedings of the 2006 ACM Symposium on Applied Computing (SAC ’06) (Dijon, France), 2006, pp. 575-581.
261. K. Oatley and J. Jenkins, Understanding emotions, Blackwell, 1996.
262. J. O’Donovan and B. Smyth, Trust in recommender systems, Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI ’05) (San Diego, CA), ACM, 2005, pp. 167-174.
263. M. O’Mahony, N. Hurley, N. Kushmerick, and G. Silvestre, Collaborative recommendation: A robustness analysis, ACM Transactions on Internet Technology 4 (2004), no. 4, 344-377.
264. M. P. O’Mahony, N. J. Hurley, and G. C. M. Silvestre, Recommender systems: Attack types and strategies, Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference (Pittsburgh, PA) (Manuela M. Veloso and Subbarao Kambhampati, eds.), July 2005, pp. 334-339.
265. T. O’Reilly, What is Web 2.0: Design patterns and business models for the next generation of software, Communictions and Strategies, International Journal of Digital Economics 65 (2007), 17-37.
266. B. O’Sullivan, A. Papadopoulos, B. Faltings, and P. Pu, Representative explanations for over-constrained problems, Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI ’07), Vancouver, British Columbia, Canada, 2007, pp. 323-328.
267. D. O’Sullivan, B. Smyth, D. C. Wilson, K. McDonald, and A. Smeaton, Improving the quality of the personalized electronic program guide, User Modeling and User-Adapted Interaction 14 (2004), no. 1, 5-36.
268. M. Papagelis, D. Plexousakis, and T. Kutsuras, Alleviating the sparsity problem of collaborative filtering using trust inferences, Proceedings of the 3rd International Conference on Trust Management (iTrust ’05) (Paris), Lecture Notes in Computer Science, vol. 3477, Springer, 2005, pp. 224-239.
269. W. Parrot, Emotions in social psychology, Taylor and Francis, 2001.
270. J. Payne, J. Bettman, and E. Johnson, The adaptive decision maker, Cambridge University Press, 1993.
271. M. Pazzani, A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review 13 (1999), no. 5-6, 393-408.
272. M. J. Pazzani, A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review 13 (1999), no. 5-6, 393-408.
273. M. J. Pazzani, Commercial applications of machine learning for personalized wireless portals, Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’02) (Tokyo), 2002, pp. 1-5.
274. M. Pazzani, J. Muramatsu, and D. Billsus, Syskill & Webert: Identifying interesting web sites, Proceedings of the 13th National Conference on Artificial Intelligence (Portland, OR), 1996, pp. 54-61.
275. M. Pazzani and D. Billsus, Learning and revising user profiles: The identification of interesting web sites, Machine Learning 27 (1997), no. 3, 313-331.
276. M. J. Pazzani and D. Billsus, Content-based recommendation systems, The Adaptive Web (Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, eds.), Lecture Notes in Computer Science, vol. 4321, Springer, 2007, pp. 325-341.
277. E. J. Pedhazur and L. P. Schmelkin, Measurement, design and analysis: An integrated approach, Lawrence Erlbaum Associates, 1991.
278. D. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI ’00) (Stanford, CA), 2000, pp. 473-480.
279. R. Picard, Affective computing, MIT Press, Cambridge, 1997.
280. R. Plutchik and R. Hope, Circumplex models of personality and emotions, American Psychological Association, 1997.
281. H. Polat and W. Du, Privacy-preserving collaborative filtering using randomized perturbation techniques, Third IEEE International Conference on Data Mining (ICDM ’03) (Melbourne, FL), November 2003, pp. 625-628.
282. H. Polat and W. Du, SVD-based collaborative filtering with privacy, Proceedings of the 2005 ACM Symposium on Applied Computing (SAC ’05) (Santa Fe, NM), ACM, 2005, pp. 791-795.
283. M. F. Porter, An algorithm for suffix stripping, Program 14 (1980), no. 3, 130-137.
284. P. Pu and L. Chen, Trust-inspiring explanation interfaces for recommender systems, Knowledge-based Systems 20 (2007), no. 6, 542-556.
285. P. Pu, D. G. Bridge, B. Mobasher, and F. Ricci (eds.), Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08), Lausanne, Switzerland, ACM, October 2008.
286. P. Pu, L. Chen, and P. Kumar, Evaluating product search and recommender systems for e-commerce environments, Electronic Commerce Research 8 (2008), no. 1-2, 1-27.
287. P. Pu, B. Faltings, and P. Kumar, User-involved tradeoff analysis in configuration tasks, The 3rd International Workshop on User-Interaction in Constraint Satisfaction at CP’2003 (Kinsale, Ireland), 2003, pp. 85-102.
288. J. R. Quinlan, C4.5: programs for machine learning, Morgan Kaufmann, San Francisco, 1993.
289. M. Ramezani, L. Bergman, R. Thompson, R. Burke, and B. Mobasher, Selecting and applying recommendation technology, Proceedings of International Workshop on Recommendation and Collaboration, in conjunctionwith the 2008 InternationalACM Conference on Intelligent User Interfaces (IUI ’08) (Gran Canaria, Spain), ACM, January 2008.
290. A. Ranganathan and R. H. Campbell, An infrastructure for context-awareness based on first order logic, Personal Ubiquitous Computing 7 (2003), no. 6, 353-364.
291. A. Rashid, I. Albert, D. Cosley, S. Lam, S. McNee, J. Konstan, and J. Riedl, Getting to know you: Learning new user preferences in recommender systems, Proceedings of the 7th International Conference on Intelligent User Interfaces (IUI ’02) (San Francisco), ACM, 2002, pp. 127-134.
292. J. Rasinger, M. Fuchs, W. Höpken, and T. Beer, Information search with mobile tourist guides: A survey of usage intention, Information Technology and Tourism 9 (2007), no. 3/4, 177-194.
293. J. Reilly, K. McCarthy, L. McGinty, and B. Smyth, Dynamic critiquing, Proceedings of the 7th European Conference on Case-Based Reasoning (ECCBR ’04), 2004, pp. 763-777.
294. J. Reilly, K. McCarthy, L. McGinty, and B. Smyth, Incremental critiquing, Knowledge-Based Systems 18 (2005), no. 4-5, 143-151.
295. J. Reilly, J. Zhang, L. McGinty, P. Pu, and B. Smyth, A comparison of two compound critiquing systems, Proceedings of the 12th International Conference on Intelligent User Interfaces (IUI ’07) (Honolulu), ACM, 2007, pp. 317-320.
296. J. Reilly, J. Zhang, L. McGinty, P. Pu, and B. Smyth, Evaluating compound critiquing recommenders: a real-user study, Proceedings of the 8th ACMConference on Electronic Commerce (EC ’07) (San Diego, CA), ACM, 2007, pp. 114-123.
297. J. Reilly, K. McCarthy, L. McGinty, and B. Smyth, Explaining compound critiques, Artificial Intelligence Review 24 (2005), no. 2, 199-220.
298. R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence 32 (1987), no. 1, 57-95.
299. P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl, Grouplens: An open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW’94) (Chapel Hill, NC), ACM, 1994, pp. 175-186.
300. P. Resnick and H. R. Varian, Recommender systems, Communications of the ACM 40 (1997), no. 3, 56-58.
301. F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor (eds.), Recommender Systems Handbook, Springer, 2010.
302. F. Ricci and Q. Nguyen, Acquiring and revising preferences in a critique-based mobile recommender system, IEEE Intelligent Systems 22 (2007), no. 3, 22-29.
303. M. Richardson, R. Agrawal, and P. Domingos, Trust management for the semantic web, The SemanticWeb-ISWC 2003 (Sanibel Island, FL), Lecture Notes in Computer Science, vol. 2870, Springer, 2003, pp. 351-368.
304. I. Ritov and J. Baron, Status-quo and omission biases, Journal of Risk and Uncertainty 5 (1992), no. 2, 49-61.
305. R. Roe, J. Busemeyer, and T. Townsend, Multialternative decision field theory: A dynamic connectionist model of decision making, Psychological Review 108 (2001), no. 2, 370-392.
306. M. Salamo, J. Reilly, L. McGinty, and B. Smyth, Knowledge discovery from user preferences in conversational recommendation, Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD ’05) (Porto, Portugal), Lecture Notes in Computer Science, vol. 3721, Springer, 2005, pp. 228-239.
307. G. Salton, The SMARTretrieval system-experiments in automatic document processing, Prentice-Hall, 1971.
308. G. Salton, A. Wong, and C.S. Yang, A vector space model for information retrieval, Journal of the American Society for Information Science 18 (1975), no. 11, 613-620.
309. G. Salton and C. Buckley, Improving retrieval performance by relevance feedback, pp. 355-364, Morgan Kaufmann, San Francisco, 1997.
310. G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management 24 (1988), no. 5, 513-523.
311. W. Samuelson and R. Zeckhauser, Status quo bias in decision making, Journal of Risk and Uncertainty 108 (1988), no. 2, 370-392.
312. J. J. Sandvig, B. Mobasher, and R. Burke, Robustness of collaborative recommendation based on association rule mining, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 105-112.
313. J. J. Sandvig, B. Mobasher, and R. Burke, A survey of collaborative recommendation and the robustness of model-based algorithms, IEEE Data Engineering Bulletin 31 (2008), no. 2, 3-13.
314. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Application of dimensionality reduction in recommender systems-a case study, Proceedings of theACMWebKDDWorkshop (Boston), 2000.
315. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, Incremental singular value decomposition algorithms for highly scalable recommender systems, Proceedings of the 5th International Conference on Computer and Information Technology (ICCIT ’02), 2002, pp. 399-404.
316. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM Conference on Electronic Commerce (EC ’00) (Minneapolis, MN), ACM, 2000, pp. 158-167.
317. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th International Conference onWorldWideWeb (WWW ’01) (Hong Kong), ACM, 2001, pp. 285-295.
318. K. Satoh, A. Inokuchi, K. Nagao, and T. Kawamura (eds.), New Frontiers in Artificial Intelligence, JSAI 2007 Conference and Workshops, Revised Selected Papers, Lecture Notes in Computer Science, vol. 4914, Miyazaki, Japan, Springer, June 2007.
319. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, Collaborative filtering recommender systems, The Adaptive Web: Methods and Strategies of Web Personalization (P. Brusilovsky, A. Kobsa, and W. Nejdl, eds.), Lecture Notes in Computer Science, vol. 4321, Springer, Berlin Heidelberg, 2006, pp. 291-324.
320. B. Schwartz, A. Ward, J. Monterosso, S. Lyubomirsky, W. White, K. White, and R. Lehman, Maximizing versus satisficing: Happiness is a matter of choice, Journal of Personality and Social Psychology 83 (2002), no. 5, 1178-1197.
321. W. Schwinger, C. Grün, B. Pröll, W. Retschitzegger, and A. Schauerhuber, Contextawareness in mobile tourism guides, Tech. Report TR-05-04, JKU Linz, Austria, Institute of Bioinformatics, 2005.
322. N. Sebe and Q. Tian, Personalized multimedia retrieval: the new trend?, Proceedings of the International Workshop on Multimedia Information Retrieval (MIR ’07) (Augsburg, Bavaria, Germany), ACM, 2007, pp. 299-306.
323. G. Semeraro, M. Degemmis, P. Lops, and P. Basile, Combining learning and word sense disambiguation for intelligent user profiling, Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI ’07) (Hyderabad, India) (Manuela M. Veloso, ed.), January 2007, pp. 2856-2861.
324. S. Sen, J. Vig, and J. Riedl, Tagommenders: connecting users to items through tags, Proceedings of the 18th International Conference on the World Wide Web (WWW ’09) (Madrid) (JuanQuemada, Gonzalo León, Yöelle S. Maarek, andWolfgang Nejdl, eds.), 2009, pp. 671-680.
325. S. Senecal and J. Nantel, The influence of online product recommendations on consumers’ online choices, Journal of Retailing 80 (2004), no. 2, 159-169.
326. A. Seth, J. Zhang, and R. Cohen, A subjective credibility model for participatory media, Workshop Intelligent Techniques for Web Personalization and Recommender Systems (ITWP) at AAAI ’08 (Chicago), AAAI Press, 2008, pp. 66-77.
327. R. Shacham, H. Schulzrinne, S. Thakolsri, and W. Kellerer, Ubiquitous device personalization and use: The next generation of IP multimedia communications, ACM Transactions on Multimedia Computing and Communication Applications 3 (2007), no. 2, 12.
328. G. Shani, R. I. Brafman, and D. Heckerman, An MDP-based recommender system, Journal of Machine Learning Research 6 (2002), 453-460.
329. G. Shani, D. M. Chickering, and C. Meek, Mining recommendations from the web, Proceedings of the 2008 ACM Conference on Recommender Systems (Lawrence, Switzerland) (Pearl Pu, Derek G. Bridge, Bamshad Mobasher, and Francesco Ricci, eds.), ACM, 2008, pp. 35-42.
330. U. Shardanand and P. Maes, Social information filtering: Algorithms for automating “word of mouth”, Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’95), vol. 1, 1995, pp. 210-217.
331. K. Shchekotykhin, D. Jannach, G. Friedrich, and O. Kozeruk, Allright: Automatic ontology instantiation from tabular web documents, Proceedings of the 6th International Conference on Semantic Web Conference (ISWC ’07) (Busan, South Korea), 2007, pp. 466-479.
332. J. Shen, B. Cui, J. Shepherd, and K.-L. Tan, Towards efficient automated singer identification in large music databases, Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’06) (Seattle), ACM, 2006, pp. 59-66.
333. A. Shepitsen, J. Gemmell, B. Mobasher, and R. D. Burke, Personalized recommendation in social tagging systems using hierarchical clustering, Proceedings of the 2008ACM Conference on Recommender Systems (Lawrence, Switzerland) (Pearl Pu, Derek G. Bridge, Bamshad Mobasher, and Francesco Ricci, eds.), ACM, 2008, pp. 259-266.
334. B. Shilit, N. Adams, and R. Want, Context-aware computing applications, Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications (WMCSA ’94) (Santa Cruz, CA) (Maria Sigala et al., ed.), IEEE Computer Society, 1994, pp. 85-90.
335. H. Shimazu, Expertclerk: A conversational case-based reasoning tool for developing salesclerk agents in e-commerce webshops, Artificial Intelligence Review 18 (2002), no. 3-4, 223-244.
336. E. H. Shortliffe, A rule-based computer program for advising physicians regarding antimicrobial therapy selection, Proceedings of the 1974 annual ACM conference (ACM’74) (New York), ACM, 1974, p. 739.
337. M.-L. Shyu, C. Haruechaiyasak, S.-C. Chen, and N. Zhao, Collaborative filtering by mining association rules from user access sequences, Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration (WIRI ’05) (Washington, DC), IEEE Computer Society, 2005, pp. 128-135.
338. B. Sigurbjörnsson and R. van Zwol, Flickr tag recommendation based on collective knowledge, Proceeding of the 17th International Conference on World Wide Web (WWW ’08) (Beijing), ACM, 2008, pp. 327-336.
339. H. Simon, A behavioral model of choice, Quarterly Journal of Economics 69 (1955), no. 1, 99-118.
340. I. Simonson and A. Tversky, Choice in context: Tradeoff contrast and extremeness aversion, Journal of Marketing Research 29, (1992), no. 3, 281-295.
341. B. Smyth and P. Cotter, Personalized adaptive navigation for mobile portals, Proceedings of the 15th European Conference on Artificial Intelligence (ECAI ’02) (Lyon, France), 2002, pp. 608-612.
342. B. Smyth, P. Cotter, and S. Oman, Enabling intelligent content discovery on the mobile internet, Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence (IAAI ’07) (Vancouver, BC), 2007, pp. 1744-1751.
343. F. Sørmo, J. Cassens, and A. Aamodt, Explanation in case-based reasoning-perspectives and goals, Artificial Intelligence Review 24 (2005), no. 2, 109-143.
344. S. Staab, H. Werthner, F. Ricci, A. Zipf, U. Gretzel, D. R. Fesenmaier, C. Paris, and C. A. Knoblock, Intelligent systems for tourism, IEEE Intelligent Systems 17 (2002), no. 6, 53-64.
345. R. E. Stake, The art of case study research, SAGE Publications, 1995.
346. X.-F. Su, H.-J. Zeng, and Z. Chen, Finding group shilling in recommendation system, Special Interest Tracks and Posters of the 14th International Conference on World Wide Web (WWW ’05) (Chiba, Japan), ACM, 2005, pp. 960-961.
347. K. Swearingen and R. Sinha, Beyond algorithms: An HCI perspective on recommender systems, Workshop on Recommender Systems held in Conjunction with SIGIR ’01 (New Orleans), 2001.
348. M. Szomszor, C. Cattuto, H. Alani, K. O’Hara, A. Baldassarri, V. Loreto, and V. D.P. Servedio, Folksonomies, the semantic web, and movie recommendation, Workshop on Bridging the Gap between SemanticWeb andWeb 2.0 at ESWC 2007 (Innsbruck, Austria), 2007, pp. 71-84.
349. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to data mining, AddisonWesley, 2006.
350. P. Tarasewich, Designing mobile commerce applications, Communications of the ACM 46 (2003), no. 12, 57-60.
351. E. Teppan and A. Felfernig, Minimization of product utility estimation errors in recommender result set evaluations, Proceedings of the 2009 IEEE/WIC/ACMInternational Joint Conference onWeb Intelligence and IntelligentAgent Technology (WI-IAT ’09) (Milano, Italy), 2009a, pp. 20-27.
352. E. Teppan and A. Felfernig, Asymmetric dominance and compromise effects in the financial services domain, Proceedings of the 11th IEEE International Conference on Commerce and Enterprise Computing (CEC ’09) (Vienna, Austria), 2009b, pp. 57-64.
353. C. Thompson, M. Göker, and P. Langley, A personalized system for conversational recommendations, Journal of Artificial Intelligence Research 21 (2004), 393-428.
354. T. Thompson and Y. Yeong, Assessing the consumer decision process in the digital marketplace, Omega 31 (2003), no. 5, 349-363.
355. J. Tiihonen and A. Felfernig, Towards recommending configurable offerings, Workshop on Configuration Systems (ECAI ’08) (Patras, Greece), 2008, pp. 29-34.
356. N. Tintarev, Explanations of recommendations, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 203-206.
357. N. Tintarev and J. Masthoff, Effective explanations of recommendations: user-centered design, Proceedings of the 2007ACMConference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 153-156.
358. G. Torkzadeh and G. Dhillon, Measuring factors that influence the success of internet commerce, Information Systems Research 13 (2002), no. 2, 187-204.
359. R. Torres, S. M. McNee, M. Abel, J. A. Konstan, and J. Riedl, Enhancing digital libraries with Techlens, International Joint Conference on Digital Libraries (JCDL ’04) (Tucson, AZ), 2004, pp. 228-236.
360. A. Töscher, M. Jahrer, and R. Legenstein, Improved neighborhood-based algorithms for large-scale recommender systems, Proceedings of the ACM SIGKDD Workshop on Large Scale Recommenders Systems and the Netflix Prize at KDD ’08 (Las Vegas), 2008.
361. E. Tsang, Foundations of constraint satisfaction, Academic Press, London and San Diego, 1993.
362. K. H. L. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme, Tag-aware recommender systems by fusion of collaborative filtering algorithms, Proceedings of the 2008 ACM Symposium onAppliedComputing (SAC’08) (Fortaleza, Ceara, Brazil), ACM, 2008, pp. 1995-1999.
363. A. Tveit, Peer-to-peer based recommendations formobile commerce, Proceedings of the 1st International Workshop on Mobile Commerce (WMC ’01) (Rome, Italy), ACM, July 2001, pp. 26-29.
364. A. Tversky and D. Kahneman, Choices, values, and frames, American Psychologist 39 (1984), no. 4, 341-350.
365. A. Tversky and D. Kahneman, Rational choice and the framing of decisions, Journal of Business 59 (1986), no. 4, 251-278.
366. S. Uhlmann and A. Lugmayr, Personalization algorithms for portable personality, Proceedings of the 12th International Conference on Entertainment and Media in the Ubiquitous Era (MindTrek ’08) (Tampere, Finland), ACM, 2008, pp. 117-121.
367. L. Ungar and D. Foster, Clustering methods for collaborative filtering, Proceedings of the Workshop on Recommendation Systems, AAAI Technical Report WS-98-08, AAAI Press, Menlo Park, CA, 1998, pp. 114-129.
368. H. van der Heijden, G. Kotsis, and R. Kronsteiner, Mobile recommendation systems for decision making “on the go”, Proceedings of the International Conference on Mobile Business (ICMB ’05) (Sydney, Australia), 2005, pp. 137-143.
369. M. van Setten, Supporting people in finding information: Hybrid recommender systems and goal-based structuring, PhD thesis, Telematica Instituut, University of Twente, The Netherlands, 2005.
370. M. van Setten, S. Pokraev, and J. Koolwaaij, Context-aware recommendations in the mobile tourist application compass, Proceedings of the 3rd International Conference of Adaptive Hypermedia and Adaptive Web-Based Systems (AH ’04) (Eindhoven, the Netherlands), Springer, 2004, pp. 235-244.
371. P. Victor, C. Cornelis, and M. D. Cock, Enhanced recommendations through propagation of trust and distrust, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’06) (Hong Kong), 2006, pp. 263-266.
372. P. Victor, C. Cornelis, M. D. Cock, and A. M. Teredesai, Key figure impact in trustenhanced recommender systems, AI Communications 21 (2008a), no. 2, 127-143.
373. P. Victor, C. Cornelis, A. M. Teredesai, and M. D. Cock, Whom should I trust?: the impact of key figures on cold start recommendations, Proceedings of the 2008 ACM Symposium on Applied Computing (SAC ’08) (Fortaleza, Ceara, Brazil), ACM, 2008b, pp. 2014-2018.
374. V. Vlahakis, N. Ioannidis, J. Karigiannis, M. Tsotros, M. Gounaris, D. Stricker, T. Gleue, P. Daehne, and L. Almeida, Archeoguide: An augmented reality guide for archaeological sites, IEEE Computer Graphics and Applications 22 (2002), no. 5, 52-60.
375. L. V. Ahn, M. Blum, and J. Langford, Captcha: Using hard AI problems for security, in Proceedings of Eurocrypt ’2003 (Warsaw, Poland), Springer, 2003, pp. 294-311.
376. J. Wang, A. P. de Vries, and M. J. T. Reinders, Unifying user-based and item-based collaborative filtering approaches by similarity fusion, Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’06) (Seattle), ACM, 2006, pp. 501-508.
377. J. Wang, A. P. de Vries, and M. J. T. Reinders, Unified relevance models for rating prediction in collaborative filtering, ACM Transactions on Information Systems 26 (2008), no. 3, 1-42.
378. A. M. A. Wasfi, Collecting user access patterns for building user profiles and collaborative filtering, Proceedings of the 4th International Conference on Intelligent User Interfaces (IUI ’99) (Los Angeles), ACM Press, 1999, pp. 57-64.
379. S. Wasserman and K. Faust, Social network analysis: Methods and applications, Cambridge University Press, 1994.
380. Y. Z. Wei, L. Moreau, and N. R. Jennings, A market-based approach to recommender systems, ACM Transactions on Information Systems 23 (2005), no. 3, 227-266.
381. B. Weiner, Attributional thoughts about consumer behavior, Journal of Consumer Research 27 (2000), no. 3, 382-387.
382. J. Weng, C. Miao, and A. Goh, Improving collaborative filtering with trust-based metrics, Proceedings of the 2006 ACM Symposium on Applied Computing (SAC ’06) (Dijon, France), ACM, 2006, pp. 1860-1864.
383. B. Widrow and S. D. Stearns, Adaptive signal processing, Prentice-Hall, 1985.
384. Wikipedia, Slope one-Wikipedia, the free encyclopedia, 2008, (Online; accessed June 4, 2008).
385. D. Wilson and T. Martinez, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research 6 (1997), 1-34.
386. D. Winterfeldt and W. Edwards, Decision analysis and behavioral research, Cambridge University Press, 1986. Workshop on collaborative filtering, online, March 1996, http://www2.sims.berkeley.edu/resources/collab/conferences/berkeley96/agenda.html.
387. B. Xiao and I. Benbasat, E-commerce product recommendation agents: Use, characteristics, and impact, MIS Quarterly 31 (2007), no. 1, 137-209.
388. Z. Xu, Y. Fu, J. Mao, and D. Su, Towards the semantic web: Collaborative tag suggestions, Proceedings of the Collaborative Web Tagging Workshop at the 15th International World Wide Web Conference (WWW ’06) (Edinburgh, Scotland), 2006.
389. G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen, Scalable collaborative filtering using cluster-based smoothing, Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’05) (Salvador, Brazil), ACM, 2005, pp. 114-121.
390. I. Yakut and H. Polat, Privacy-preserving eigentaste-based collaborative filtering, Proceedings of the International Workshop on Security (IWSEC ’07) (Nara, Japan) (Atsuko Miyaji, Hiroaki Kikuchi, and Kai Rannenberg, eds.), Lecture Notes in Computer Science, vol. 4752, Springer, October 2007, pp. 169-184.
391. Y. Yang and X. Liu, A re-examination of text categorization methods, Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’99) (Berkley, CA), ACM, August 1999, pp. 42-49.
392. Y. Yi, The effects of contextual priming in print advertisements, Journal of Consumer Research 17 (1990), no. 2, 215-222.
393. R. K. Yin, Case study research-design and methods, 3rd ed., SAGE Publications, 2002.
394. S. Yoon and I. Simonson, Choice set configuration as a determinant of preference attribution and strength, Journal of Consumer Research 35 (2008), no. 2, 324-336.
395. K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences, Proceedings of 7th International Conference on Music Information Retrieval (ISMIR ’06) (Victoria, BC, Canada), 2006, pp. 296-301.
396. K. Yu, A. Schwaighofer, V. Tresp, X. Xu, and H.-P. Kriegel, Probabilistic memory-based collaborative filtering, IEEE Transactions on Knowledge and Data Engineering 16 (2004), no. 1, 56-69.
397. K. Yu, X. Xu, M. Ester, and H.-P. Kriegel, Feature weighting and instance selection for collaborative filtering: An information-theoretic approach, Knowledge Information Systems 5 (2003), no. 2, 201-224.
398. V. Zanardi and L. Capra, Social ranking: Uncovering relevant content using tag-based recommender systems, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), ACM Press, 2008, pp. 51-58.
399. M. Zanker, A collaborative constraint-based meta-level recommender, Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys ’08) (Lausanne, Switzerland), ACM Press, 2008, pp. 139-146.
400. M. Zanker, M. Aschinger, and M. Jessenitschnig, Development of a collaborative and constraint-based web configuration system for personalized bundling of products and services, Proceedings of the 8th International Conference on Web Information Systems Engineering (WISE ’07) (Nancy, France), Springer, 2007, pp. 273-284.
401. M. Zanker, M. Bricman, S. Gordea, D. Jannach, and M. Jessenitschnig, Persuasive online-selling in quality & taste domains, Proceedings of the 7th International Conference on Electronic Commerce and Web Technologies (EC-Web ’06) (Krakow, Poland), Springer, 2006, pp. 51-60.
402. M. Zanker, M. Fuchs, W. Höpken, M. Tuta, and N. Müller, Evaluating Recommender Systems in Tourism-A Case Study from Austria, Proceedings of the International Conference on Information and Communication Technologies in Tourism (ENTER) (Innsbruck, Austria), 2008, pp. 24-34.
403. M. Zanker, M. Jessenitschnig, and M. Fuchs, Automated semantic annotations of tourism resources based on geospatial data, Information Technology & Tourism, 11 (2009), no. 4, 341-354.
404. M. Zanker, S. Gordea, M. Jessenitschnig, and M. Schnabl, A hybrid similarity concept for browsing semi-structured product items, E-Commerce andWeb Technologies, 7th International Conference (EC-Web 2006) (Krakow, Poland) (Kurt Bauknecht, Birgit Pröll, and Hannes Werthner, eds.), Lecture Notes in Computer Science, vol. 4082, Springer, 2006, pp. 21-30.
405. M. Zanker and M. Jessenitschnig, Case-studies on exploiting explicit customer requirements in recommender systems, User Modeling and User-Adapted Interaction 19 (2009), no. 1-2, 133-166.
406. M. Zanker and M. Jessenitschnig, Collaborative feature-combination recommender exploiting explicit and implicit user feedback, Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (CEC ’09) (Vienna), IEEE Computer Society, 2009, pp. 49-56.
407. M. Zanker, M. Jessenitschnig, and W. Schmid, Preference Reasoning with Soft Constraints in Constraint-Based Recommender Systems, Constraints, Springer, 15 (2010), no. 4, 574-595.
408. M. Zanker, M. Jessenitschnig, D. Jannach, and S. Gordea, Comparing recommendation strategies in a commercial context, IEEE Intelligent Systems 22 (2007), no. 3, 69-73.
409. P. Zezula, G. Amato, V. Dohnal, and M. Batko, Similarity search-the metric space approach, Advances in Database Systems, vol. 32, Springer, 2006.
410. J. Zhang and P. Pu, A recursive prediction algorithm for collaborative filtering recommender systems, Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys ’07) (Minneapolis, MN), ACM, 2007, pp. 57-64.
411. S. Zhang, A. Chakrabarti, J. Ford, and F. Makedon, Attack detection in time series for recommender systems, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’06) (Philadelphia), ACM, 2006, pp. 809-814.
412. S. Zhang, J. Ford, and F. Makedon, Deriving private information from randomly perturbed ratings, Proceedings of the 6thSIAMInternationalConference on DataMining (SDM ’06) (Bethesda, MD) (Joydeep Ghosh, Diane Lambert, David B. Skillicorn, and Jaideep Srivastava, eds.), SIAM, April 2006, pp. 59-69.
413. S. Zhang, J. Ford, and F. Makedon, A privacy-preserving collaborative filtering scheme with two-way communication, Proceedings of the 7th ACM Conference on Electronic Commerce (EC ’06) (Ann Arbor, MI), ACM, 2006, pp. 316-323.
414. Y. Zhang, J. Callan, and T. Minka, Novelty and redundancy detection in adaptive filtering, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’02) (Tampere, Finland), ACM, 2002, pp. 81-88.
415. Y. Zhen, W.-J. Li, and D.-Y. Yeung, TAGICOFI: tag informed collaborative filtering, Proceedings of the 2009 ACM Conference on Recommender Systems (RecSys ’09) (New York), 2009, pp. 69-76.
416. C.-N. Ziegler and G. Lausen, Spreading activation models for trust propagation, Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE ’04) (Taipei), IEEE Computer Society, March 2004, pp. 83-97.
417. C.-N. Ziegler and G. Lausen, Propagation models for trust and distrust in social networks, Information Systems Frontiers 7 (2005), no. 4-5, 337-358.
418. C.-N. Ziegler, G. Lausen, and L. Schmidt-Thieme, Taxonomy-driven computation of product recommendations, Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management (CIKM ’04) (Washington, DC), 2004, pp. 406-415.
419. C.-N. Ziegler, S. M. Mcnee, J. A. Konstan, and G. Lausen, Improving recommendation lists through topic diversification, Proceedings of the 14th International Conference on World Wide Web (WWW ’05) (New York), ACM Press, 2005, pp. 22-32.
420. A. Zimmermann, M. Specht, and A. Lorenz, Personalization and context management, User Modeling and User-Adapted Interaction 15 (2005), no. 3-4, 275-302.