Mediation of user models for enhanced personalization in recommender systems

User Modeling and User-Adapted Interaction

Volumn 18, Issue 3, 2008, Pages 245-286

  Berkovsky, Shlomo ^a   Kuflik, Tsvi ^b   Ricci, Francesco ^c  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b UNIVERSITY OF HAIFA   (Israel)

^c FREE UNIVERSITY OF BOZEN BOLZANO   (Italy)

Author keywords

Mediation of user modeling data; Recommender systems; Ubiquitous user modeling

Indexed keywords

PERSONALIZED RECOMMENDATIONS; RECOMMENDER SYSTEMS;

DATA ACQUISITION; MATHEMATICAL MODELS; UBIQUITOUS COMPUTING;

USER INTERFACES;

EID: 45049087177     PISSN: 09241868     EISSN: 15731391     Source Type: Journal    
DOI: 10.1007/s11257-007-9042-9     Document Type: Article

References (73)

1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. 23(1), 103-145 (2005)
2. Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Eng. 17(6), 734-749 (2005a)
3. Adomavicius, G., Tuzhilin, A.: Personalization technologies: a process-oriented perspective. Commun. ACM 48(10), 83-90 (2005b)
4. Aguzzoli, S., Avesani, P., Massa, P.: Collaborative case-based recommender system. In: European Conference on Case-Based Reasoning, pp. 460-474. Aberdeen, UK (2002)
5. Aroyo, L., Schut, H., Nack, F., Schiphorst, T., Kauw-A-Tjoe, M.: Personalized ambient media experience: move.me case study. In: International Conference on Intelligent User Interfaces, pp. 298-301. Honolulu, HI (2007)
6. Berkovsky, S.: Decentralized mediation of user models for a better personalization. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 404-408. Dublin, Ireland (2006)
7. Berkovsky, S., Kuflik, T., Ricci, F.: Cross-technique mediation of user models. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 21-30. Dublin, Ireland (2006a)
8. Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Recommender Systems Conference Minneapolis, MN, (2007c)
9. Berkovsky, S., Aroyo, L., Heckmann, D., Houben, G.J., Kröner, A., Kuflik, T., Ricci, F.: Providing context-aware personalization through cross-context reasoning of user modeling data. In: Workshop on Decentralized and Ubiquitous User Modeling, pp. 2-7. Corfu, Greece (2007a)
10. Berkovsky, S., Gorfinkel, A., Kuflik, T., Manevitz, L.: Case-based to content-based user model mediation. In: Workshop on Ubiquitous User Modeling, pp. 1-4. Riva del Garda, Italy (2006b)
11. Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: Recommender Systems Conference, Minneapolis, MN (2007b)
12. Brown, P.J., Bovey, J.D., Chen, X.: Context-aware applications: from the laboratory to the marketplace. IEEE Pers. Commun. 4(5), 58-64 (1997)
13. Buriano, L., Marchetti, M., Carmagnola, F., Cena, F., Gena, C., Torre, I.: The role of ontologies in context-aware recommender systems. In: International Conference on Mobile Data Management, p. 80. Nara, Japan (2006)
14. Burke, R.: Knowledge-based recommender systems. In: Kent A. (ed.) Encyclopedia of Library and Information Systems, pp. 180-200. Dekker Encyclopedias (2000)
15. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapt. Interact. 12(4), 331-370 (2002)
16. Chen, A.: Context-aware collaborative filtering system: predicting the user's preference in the ubiquitous computing environment. In: International Workshop on Location- and Context-Awareness, pp. 244-253. Oberpfaffenhofen, Germany (2005)
17. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Workshop on Recommender Systems, Berkeley, CA, http://www.cs.umbc.edu/~ian/ sigir99-rec/ (1999)
18. Cranor, L.F., Reagle, J., Ackerman, M.S.: Beyond concern: understanding net users' attitudes about online privacy. Technical Report, AT&T Labs-Research (1999)
19. Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing, pp. 304-307. Karlsruhe, Germany (1999)
20. Finin, T.W., Drager, D.: A general user modeling system. In: Canadian Conference on Artificial Intelligence, pp. 24-29. Montreal, Canada (1986)
21. Finin, T.W.: GUMS: a general user modeling shell. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, pp. 411-430. Springer Publishers (1989)
22. Francisco-Revilla, L., Shipman, M.: Managing conflict in multi-model adaptive hypertext. In: Conference on Hypertext and Hypermedia, pp. 237-238. Santa Cruz, CA (2004)
23. Goker, A., Myrhaug, H.I.: User context and personalization. In: Workshop on Case Based Reasoning and Personalization, pp. 4-7. Aberdeen, UK (2002)
24. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inform. Retr. 4(2), 133-151 (2001)
25. Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61-70 (1992)
26. Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B. Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: National Conference on Artificial Intelligence, pp. 439-446. Orlando, FL (1999)
27. Hanani, U., Shapira, B., Shoval, P.: Information filtering: overview of issues, research and systems. User Model. User Adapt. Interact. 11(3), 203-259 (2001)
28. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., Wilamowitz-Moellendorff, M.V.: GUMO-the general user model ontology. In: International Conference on User Modeling, pp. 428-432. Edinburgh, UK (2005)
29. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: International Conference on Research and Development in Information Retrieval, pp. 230-237. Berkeley, CA (1999)
30. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Syst. 22(1), 5-53 (2004) Internet Movie Database, http://www.imdb.com
31. Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: a tour guide for the World Wide Web. In: International Joint Conference on Artificial Intelligence, pp. 770-777. Nagoya, Japan (1997)
32. Keidl, M., Kemper, A.: Towards context-aware adaptable web services. In: International World-Wide Web Conference, pp. 55-65. New York, NY (2004)
33. Kay, J.: Ontologies for reusable and scrutable student model. In: Workshop on Workshop on Ontologies for Intelligent Educational Systems, pp. 72-77. Le Mans, France (1999)
34. Kay, J., Kummerfeld, B., Lauder, P.: Managing private user models and shared personas. In: Workshop on User Modeling in Ubiquitous Computing, pp. 1-11. Pittsburgh, PA (2003)
35. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20(3), 226-239 (1998)
36. Kobsa, A.: Generic User Modeling Systems. User Model. User-Adapt. Interact. 11(1-2), 49-63 (2001)
37. Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowledge Eng. Rev. 16(2), 111-155 (2001)
38. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. J. 97(1-2), 273-324 (1997)
39. Krulwich, B.: Lifestyle finder: intelligent user profiling using large-scale demographic data. Artif. Intell. Mag. 18(2), 37-45 (1997)
40. Kuflik, T., Sheidin, J., Jbara, S., Goren-Bar, D., Soffer, P., Stock, O., Zancanaro M.: Supporting small groups in the museum by context-aware communication services. In: International Conference on Intelligent User Interfaces, pp. 305-308. Honolulu, HI (2007)
41. Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative filtering and inference rules for context-aware learning object recommendation. J. Interact. Technol. Smart Educ. 2(3), 179-188 (2005)
42. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76-80 (2003)
43. Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 31-40 (1994)
44. Manouselis, N., Sampson, D.: A multi-criteria model to support automatic recommendation of e-learning quality approaches. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 518-526. Chesapeake, VA (2004)
45. McJones, P.: EachMovie Collaborative Filtering Data Set. HP Research (1997)
46. McNee, S.M., Lam, S., Konstan, J., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: International Conference on User Modeling, pp. 178-183. Pittsburgh, PA (2003)
47. Mehta, B., Niederée, C., Stewart, A., Degemmis, M., Lops, P., Semeraro, G.: Ontologically enriched user profiling for cross system personalization. In: International Conference on User Modeling, pp. 119-123. Edinburgh, UK (2005)
48. Montaner, M., Lopez, B., Rosa, J.L.: A taxonomy of recommender agents on the internet. AI Rev. 19(4), 285-330 (2003)
49. Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match retrieval. In: International Conference on Research and Development in Information Retrieval, pp. 272-281. Dublin, Ireland (1994)
50. Mulvenna, M.D., Anand, S.S., Buchner, A.G.: Personalization on the net using web mining. Commun. ACM 43(8), 123-125 (2000)
51. Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Investigation for designing of context-aware recommendation system using SVM. In: International MultiConference of Engineers and Computer Scientists, pp. 970-975. Hong Kong, Hong Kong (2007)
52. Pascoe, J.: Adding generic contextual capabilities to wearable computers. In: International Symposium on Wearable Computers, pp. 92-99. Pittsburgh, PA (1998)
53. Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393-408 (1999)
54. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313-331 (1997)
55. Potonniee, O.: A decentralized privacy-enabling TV personalization framework. In: European Conference on Interactive Television, pp. 57-66. Brighton, UK (2002)
56. Rabanser, U., Ricci, F.: Recommender systems: do they have a viable business model in e-tourism? In: Conference of the International Federation for IT & Travel and Tourism, pp. 160-171. Lausanne, Switzerland (2005)
57. Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based user modeling for knowledge management systems. In: International Conference on User Modeling, pp. 213-217. Pittsburgh, PA (2003)
58. Resource Description Framework, http://www.w3c.org/RDF/
59. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of Netnews. In: International Conference on Computer-Supported Collaborative Work, pp. 175-186. Chapel Hill, NC (1994)
60. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56-58 (1997)
61. Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: a case-based travel advisory system. In: European Conference on Case-Based Reasoning, pp. 613-627. Aberdeen, UK (2002)
62. Ricci, F.: Recommendations in context. In: International Conference on Electronic Commerce and Web Technologies, Krakow, Poland, keynote talk (2006)
63. Ricci, F., Cavada, D., Mirzadeh, N., Venturini, A.: Case-based travel recommendations. In: Fesemaier, D.R., Werthner, H., Wöber, K.W. (eds.) Destination Recommendation Systems: Behavioural Foundations and Applications, pp. 67-93. CABI publishers (2006)
64. Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles for user behaviors. Comput. Networks ISDN Syst. 29(8-13), 1447-1455 (1997)
65. Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill publishers (1983)
66. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering algorithms. In: International World-Wide Web Conference, pp. 285-295. Hong Kong, Hong Kong (2001)
67. Schafer, J.B., Konstan, J., Riedl, J.: Electronic commerce recommender applications. J. Data Mining Knowledge Discov. 5(1-2), 115-152 (2000)
68. Setten, M.V., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 235-244. Eindhoven, the Netherlands (2004)
69. Shardanand, U., Maes, P.: Social information filtering: algorithms for automating 'word of mouth'. In: International Conference on Human Factors in Computing Systems, pp. 210-217. Pittsburgh, PA, (1995)
70. Vozalis, M., Margaritis, K.G.: Enhancing collaborative filtering with demographic data: the case of item-based filtering. In: International Conference on Intelligent Systems Design and Applications, pp. 361-366. Budapest, Hungary (2004)
71. Wahlster, W., Kröner, A., Heckmann, D.: SharedLife: towards selective sharing of augmented personal memories. In: Sotck, O., Schaerf, M. (eds.) Reasoning, Action and Interaction in AI Theories and Systems, pp. 327-342. Springer Publishers (2006)
72. Wang, Y., Kobsa, A.: Impacts of privacy laws and regulations on personalized systems. In: Workshop on Privacy-Enhanced Personalization, pp. 44-46. Montreal, Canada (2006)
73. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: International World-Wide Web Conference, pp. 22-32. Chiba, Japan (2005)