What's on TV Tonight? An Efficient and Effective Personalized Recommender System of TV Programs

IEEE Transactions on Consumer Electronics

Volumn 55, Issue 1, 2009, Pages 286-294

  Martínez, Ana Belén Barragáns ^a   Arias, José J Pazos ^a   Vilas, Ana Fernández ^a   Duque, Jorge García ^a   Nores, Martín López ^a  

^a NONE

Author keywords

Collaborative filtering; Contents personalization; Recommender systems; SVD

Indexed keywords

EID: 85008036624     PISSN: 00983063     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCE.2009.4814447     Document Type: Article

References (27)

1. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans, on Knowl. and Data Eng., 17(6):734–749, 2005.
2. P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, 40(3):56–58, 1997.
3. th National Conference on Artificial Intelligence (AAAI'98), pages 714–720, 1998.
4. R. Burke, “Hybrid recommender systems: survey and experiments,” User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.
5. B. Smyth and P. Cotter, “A personalized television listing service,” Communications of the ACM, 43(8): 107–111, 2000.
6. 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(1): 103–145, 2005.
7. M. Balabanovic and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM, 40(3):66–72, 1997.
8. nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230–237, New York, NY, USA, 1999. ACM Press.
9. th Conference on Uncertainty in Artificial Intelligence, pages 43–52, 1998.
10. P. Resnick, N. Iacovou, M. Suchack, P. Bergstrom and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” In Proceedings of the ACM Conference on Computer Supported Cooperative Work, pages 175–186, 1994.
11. K. Goldberg, T. Roeder, D. Gupta and C. Perkins, “Eigentaste: a constant time collaborative filtering algorithm,” Information Retrieval, 4(2):133–151, 2001.
12. B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of dimensionality reduction in recommender system — A case study,” In Proc. of the Workshop on Web Mining for E-Commerce — Challenges and Opportunities (WEBKDD' 00), Boston, USA, August 20, 2000.
13. M. G. Vozalis and K. G. Margaritis, “Identifying the effects of SVD and demographic data use on generalized collaborative filtering”. International Journal of Computer Mathematics, 85(12): 1741–1763, 2008.
14. G. Lekakos and G. M. Giaglis, “Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors,” Interacting with Computers, 18(3):410–431, 2006.
15. J. L. Herlocker, J. A. Konstan and J. Riedl, “Explaining collaborative filtering recommendations,” In Proceedings of the ACM conference on Computer Supported Cooperative Work, pages 241–250, 2000.
16. T. Tsunoda and M. Hoshino, “Automatic metadata expansion and indirect collaborative filtering for TV program recommendation system,” Multimedia Tools Appl., 36:37–54, 2008.
17. D. L. Lee, H. Chuang and K. E. Seamons, “Document ranking and the vector-space model,” IEEE Software, 14(2):67–75, 1997.
18. rd edition, 1996.
19. th International Conference on Intelligent Systems Design and Applications (ISDA'05), pages 464–469, 2005.
20. J. Herlocker, J. A. Konstan and J. Riedl, “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms,” Information Retrieval, 5(4):287–310, 2002.
21. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, 22(1):5–53, 2004.
22. nd Workshop on Personalization in Future TV, pages 23–32, 2002.
23. th International Symposium on Multimedia Software Engineering (MSE'02), pages 82–89, 2002.
24. P. Baudisch, “Dynamic Information Filtering,” Ph.D. Thesis. GMD Research Series, 2001, No. 16. GMD Forschungszentrum Informationstechnik GmbH, Sankt Augustin.
25. A. M. Rashid, K. Ling, R. D. Tassone, P. Resnick, R. Kraut, and J. Riedl, “Motivating participation by displaying the value of contribution,” In Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI'06), pages 955–958, New York, NY, USA, 2006. ACM Press.
26. M. Harper, S. X. Li, Y. Chen and J. A. Konstan, “Social comparisons to motivate contributions to an online community,” In Second International Conference on Persuasive Technology (PERSUASIVE'07), pages 148–159, 2007.
27. M. Zanker and M. Jessenitschnig, “Case-studies on exploiting explicit customer requirements in recommender systems,” User Modeling and User-Adapted Interaction: The Journal of Personalization Research, A. Tuzhilin and B. Mobasher (Eds.): Special issue on Data Mining for Personalization, 19(1), 2009.