On unexpectedness in recommender systems: Or how to expect the unexpected

CEUR Workshop Proceedings

Volumn 816, Issue , 2011, Pages 11-18

  Adamopoulos, Panagiotis ^a   Tuzhilin, Alexander ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

Recommender Systems; Unexpectedness; Utility Theory

Indexed keywords

BASELINE METHODS; BUSINESS SUCCESS; QUALITY OF RECOMMENDATIONS; STANDARD BASELINE METHODS; UNEXPECTEDNESS; USER SATISFACTION; UTILITY FUNCTIONS; UTILITY THEORY;

RECOMMENDER SYSTEMS;

EID: 84892557158     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems, 5th ACM Conf. on Recommender Systems (RecSys 2011) http://ir. ii. uam. es/hetrec2011
2. Adomavicius, G., & Kwon, Y. Toward more diverse recommendations: Item re-ranking methods for recommender systems. In WITS (2009)
3. Adomavicius, G., & Kwon, Y. Improving aggregate recommendation diversity using ranking-based techniques. IEEE TKDE (2011), pp. 1-15
4. Adomavicius, G., & Tuzhilin, A. Toward the next generation of recommender systems: A Survey of the state-of-the-art and possible extensions. IEEE TKDE (2005), pp. 734-749
5. Akiyama, T., Obara, T., & Tanizaki, M. Proposal and evaluation of serendipitous recommendation method using general unexpectedness. In PRSAT RecSys (2010)
6. Bell, R., Bennett, J., Koren, J., & Volinsky, C. The million dollar programming prize. IEEE Spectrum 46, 5 (2009)
7. Bennett, J., & Lanning, S. The Netflix Prize. In KDD (2007)
8. Billsus, D., & Pazzani, M. User modeling for adaptive news access. UMUAI 10, 2-3 (2000), pp. 147-180
9. Burke, R. Hybrid recommender systems: Survey and experiments. UMUAI 12, 4 (2002), pp. 331-370
10. Cremer, H. & Thisse, J. F. Location models of horizontal differentiation: A special case of vertical differentiation models. The Journal of Industrial Economics 39, 4 (1991)
11. Ge, M., Delgado-Battenfeld, C., & Jannach, D. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. In RecSys (2010)
12. GroupLens Research. http://www. grouplens. org
13. Herlocker, J., Konstan, J., Terveen, L., & Riedl J. Evaluating collaborative filtering recommender systems. ACM TOIS 22, 1 (2004), pp. 5-53
14. Hijikata, Y., Shimizu, T., & Nishida, S. Discovery-oriented collaborative filtering for improving user satisfaction. IUI (2009), pp. 67-76
15. Iaquinta, L., Gemmis, M. D., Lops, P., Semeraro, G., Filannino, M., & Molino, P. Introducing serendipity in a content-based recommender system. In HIS (2008)
16. Kawamae, N., Sakano, H., & Yamada, T. Personalized recommendation based on the personal innovator degree. In RecSys (2009)
17. Konstan, J., McNee, S., Ziegler, C. N., Torres, R., Kapoor, N., & Riedl, J. Lessons on applying automated recommender systems to information-seeking tasks. In AAAI (2006)
18. Marshall, A. Principles of Economics, 8th ed. Macmillan and Co., London, UK, (1926)
19. McNee, S., Riedl, J., & Konstan, J. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In CHI (2006)
20. Murakami, T., Mori, K., & Orihara, R. Metrics for evaluating serendipity of recommendation lists. In JSAI (2007)
21. Neven, D. Two stage equilibrium in Hotelling's model. The Journal of Industrial Economics 33, 3 (1985), pp. 317-325
22. Padmanabhan, B., & Tuzhilin, A. A belief-driven method for discovering unexpected patterns. KDD (1998), pp. 94-100
23. Padmanabhan, B., & Tuzhilin, A. Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems 27, 3 (1999), pp. 303-318
24. Shani, G., & Gunawardana, A. Evaluating recommendation systems, In Recommender Systems Handbook, Springer-Verlag, New York, NY, USA, (2011), pp. 257-297
25. Tirole, J. Product differentiation: Price competition and nonprice competition. The Theory of Industrial Organization. The MIT Press, Cambridge, USA, (1988)
26. Zhang, M., & Hurley, N. Avoiding monotony: Improving the diversity of recommendation lists. In RecSys (2008)
27. Ziegler, C. N., McNee, S., Konstan, J., & Lausen, G. Improving recommendation lists through topic diversification. In WWW (2005)
28. Weng, L. T., Xu, Y., Li, Y., & Nayak, R. Improving recommendation novelty based on topic taxonomy. WIC (2007), pp. 115-118.