Constraint-based recommender systems: Technologies and research issues

ACM International Conference Proceeding Series

Volumn , Issue , 2008, Pages

  Felfernig, A ^a   Burke, R ^b  

^a UNIVERSITY OF KLAGENFURT   (Austria)

^b DEPAUL UNIVERSITY   (United States)

Author keywords

Constraint based recommendation

Indexed keywords

AI APPLICATIONS; ALGORITHMIC APPROACHES; COMPLEX PRODUCTS; CONSTRAINT-BASED RECOMMENDATION; E COMMERCES; KNOWLEDGE SOURCES; PRODUCTS AND SERVICES; RECOMMENDER SYSTEMS; RESEARCH ISSUES;

TAXONOMIES;

ELECTRONIC COMMERCE;

EID: 65449158451     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1409540.1409544     Document Type: Conference Paper

References (41)

1. Aciar S., Zhang, D., Simoff, S., and Debenham, J. 2007. Informed Recommender: Basing Recommendations on Consumer Product Reviews, IEEE Intelligent Systems Special Issue: Recommender Systems, 22(3):39-47.
2. Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: a survey of the state-ofthe-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6): 734-749.
3. th ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, pp. 205-212.
4. th International WWW Conference, Brisbane, Australia, pp.107-117.
5. Burke, R., Hammond, K., and Young, B. 1997. The FindMe Approach to Assisted Browsing. IEEE Expert, 12(4), pages 32-40.
6. Burke, R. 2000. Knowledge-based recommender systems. Encyclopedia of Library & InformationSy stems, 69(32).
7. Burke, R. 2002. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction 12(4):331-370.
8. st National Conference on Artificial Intelligence (AAAI'06), pp. 157-162, Boston, USA.
9. Coester, R., Gustavsson, A., Olsson, R., and Rudstroem, A. 2002. Enhancing web-based configuration with recommendations and cluster-based help', in AH'02 Workshop on Recommendation and Personalization in ECommerce, Malaga, Spain.
10. Felfernig, A., Friedrich, G., Isak, K., Shchekotykhin, K., Teppan, E, and Jannach, D. 2008. Automated Debugging of Recommender User Interface Descriptions, to appear: Journal of Applied Intelligence.
11. Felfernig, A., Friedrich, G., Teppan, E., and Isak, K 2008. Intelligent Debugging and Repair of Utility Constraint Sets in Knowledge-based Recommender Applications, to appear 13th ACM International Conference on Intelligent User Interfaces, Canary Islands, Spain.
12. Felfernig, A. 2007. Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization, IEEE Transactions on Engineering Management, 54(1):41-56.
13. Felfernig, A. 2007. Reducing Development and Maintenance Efforts for Web-based Recommender Applications, International Journal of Web Engineering and Technology, 3(3):329-351.
14. Felfernig, A., Friedrich, G., Schmidt-Thieme, L. 2007. Introduction to the IEEE Intelligent Systems Special Issue: Recommender Systems, 22(3): 18-21.
15. Felfernig, A., Isak, K., Szabo, K., and Zachar, P. 2007. The VITA Financial Services Sales Support Environment, AAAI/AAI 2007, pp. 1692-1699, Vancouver, Canada.
16. Felfernig, A., Friedrich, G., Gula, B., Hitz, M., Kruggel, T., Melcher, R., Riepan, D., Strauss, S., Teppan, E., and Vitouch, O. 2007. Persuasive Recommendation: Exploring Serial Position Effects in Knowledge-based Recommender Systems, Persuasive 2007, LNCS, 4744.
17. Herlocker, J., Konstan, J., Terveen, L., and Riedl, J. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5-53.
18. Jiang, B., Wang, W., and Benbasat, I. 2005. Multimedia-Based Interactive Advising Technology for Online Consumer Decision Support, Communications of the ACM. 48 (9), 93-98.
19. th National Conference on AI (AAAI'04), pp. 167-172.
20. Kelleher, J. and Bridge, D. 2004. An Accurate and Scalable Collaborative Recommender, Artificial Intelligence Review, 21(3-4):193-213.
21. Konstan, J., Miller, B., Herlocker, J., Gordon, L., and Riedl, J. 1997. GroupLens: Applying Collaborative Filtering to Usenet News, Communications of the ACM, 40(3): 77-87.
22. Kuoa, F., Chub, T., Hsuc, M., Hsieha, H. 2004. An investigation of effort-accuracy trade-off and the impact of self-efficacy on Web searching behaviors, Decision Support Systems, 37: 331-342, Elsevier.
23. Martin, B., Sherrard, M., Wentzel, D. 2005.The Role of Sensation Seeking and Need for Cognition on Web-Site Evaluations: A Resource-Matching Perspective, Psychology and Marketing, 22(2): 109-126, Wiley.
24. McSherry, D. 2004. Maximally Successful Relaxations of Unsuccessful Queries. 15th Conference on Artificial Intelligence and Cognitive Science, Galway, Ireland, pp. 127-136.
25. Mirzadeh, N., Ricci, F., Bansal, M. 2005. Feature Selection Methods for Conversational Recommender Systems, IEEE International Conference on e-Technology, e-Commerce, ande-Service (EEE'05), Hongkong, 772-777.
26. rd International Conf. on Information Technology, Las Vegas, Nevada, pp. 388-393.
27. Payne, J., Bettman, J., and Johnson, E. 1993. The Adaptive Decision Maker. Cambridge University Press.
28. Pazzani, M., and Billsus, D. 1997. Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning. (27), 313-331, (1997).
29. Pine, B., and Davis, S. 1999. Mass Customization: The New Frontier in Business Competition. Harvard Business School Press, 1999.
30. Reiter, R. 1987. A theory of diagnosis from first principles. Artificial Intelligence, 23(1):57-95, 1987.
31. Resnick, P. and Varian, H. R. 1997. Recommender Systems, Communications of the ACM, 40(8):56-58.
32. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175-186.
33. Ricci, F., and Werthner, H. 2006. Special Issue of International Journal of Electronic Commerce, on Recommender Systems, 11(2).
34. Ricci, F. and Q. Nguyen. 2007. Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System, IEEE Intelligent Systems Special Issue: Recommender Systems, 22(3):22-29.
35. th International WWW Conference, Hongkong, pp. 285-295.
36. Schafer, J., Konstan, J., and Riedl, J. 2000. Electronic Commerce recommender applications. Journal of Data Mining and Knowledge Discovery, 5(1/2): 115-152.
37. Simon, H. 1955. A Behavioral Model of Choice. Quarterly Journal of Economics, 69(1): 99-118.
38. Smyth, B., McGinty, L., Reilly, J., McCarthy, K. 2004. Compound Critiques for Conversational Recommender Systems, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04), Beijing, China, pp. 145 - 151.
39. Thompson, C., Göker, M., and Langley, P. 2004. A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21:393-428.
40. Tsang, E. 1993. Foundations of Constraint Satisfaction, Academic Press, London and San Diego.
41. Winterfeldt, D., and Edwards, W. Decision Analysis and Behavioral Research, Cambridge University Press, Cambridge, England, 1986.