State-of-the-art recommender systems

Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

Volumn , Issue , 2009, Pages 1-22

  Candillier, Laurent ^a   Jack, Kris ^a   Fessant, Françoise ^a   Meyer, Frank ^a  

^a ORANGE LABS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84900174849     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60566-306-7.ch001     Document Type: Chapter

References (46)

1. Adomavicius, G., & Tuzhilin, A. (2005). 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(6), 734-749.
2. Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
3. Basu, C., Hirsh, H., & Cohen, W. W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In 15th National Conference on Artificial Intelligence (pp. 714-720).
4. Bell, R., Koren, Y., & Volinsky, C. (2007). Modeling relationships at multiple scales to improve accuracy of large recommender systems. In 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 95-104). New York, NY, USA. ACM.
5. Bilgic, M. (2004). Explanation for Recommender Systems: Satisfaction vs. Promotion. PhD thesis, University of Texas at Austin, Department of Computer Sciences.
6. rd International Conference on Autonomous Agents (pp. 268-275). ACM Press.
7. Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In 14th Conference on Uncertainty in Artificial Intelligence (pp. 43-52). Morgan Kaufman.
8. Candillier, L., Meyer, F., & Boullé, M. (2007). Comparing state-of-the-art collaborative filtering systems. In Perner, P. (Ed.), 5th International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 548-562), Leipzig, Germany. Springer Verlag.
9. Cilibrasi, R., & Vitanyi, P.M.B. (2007). The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370-383.
10. Cohn, D. A., Ghahramani, Z., & Jordan M. I. (1996). Active Learning with Statistical Models. In Journal of Artificial Intelligence Research, 4, 129-145.
11. Deshpande, M., & Karypis, G. (2004). Itembased top-N recommendation algorithms. In ACM Transactions on Information Systems, 22(1), 143-177.
12. th Conference of Information and Knowledge Management (pp. 446-452).
13. Herlocker, J., Konstan, J., & Riedl, J. (2000). Explaining collaborative filtering recommendations. In ACM Conference on Computer Supported Cooperative Work.
14. Herlocker, J., Konstan, J., Terveen, L., & Riedl, J. (2004). Evaluating collaborative filtering recommender systems. In ACM Transactions on Information Systems, 22(1), 5-53.
15. Jack, K., & Duclaye, F. (2007). Etude de la pertinence de critères de recherche en recherche d'informations sur des données structurées. In PeCUSI, INFORSID (pp. 285-297). Perros-Guirec, France.
16. Jack, K., & Duclayee, F. (2008). Improving Explicit Preference Entry by Visualising Data Similarities. In Intelligent User Interfaces, International Workshop on Recommendation and Collaboration (ReColl). Spain.
17. Karypis, G. (2001). Evaluation of item-based topN recommendation algorithms. In 10th International Conference on Information and Knowledge Management (pp. 247-254).
18. Kelleher, J., & Bridge, D. (2003). Rectree centroid: An accurate, scalable collaborative recommender. In Cunningham, P., Fernando, T., & Vogel, C. (Ed.), 14th Irish Conference on Artificial Intelligence and Cognitive Science (pp. 89-94).
19. Kleinberg, J., & Sandler, M. (2004). Using mixture models for collaborative filtering. In 36th ACM Symposium on Theory of Computing (pp. 569-578). ACM Press.
20. Krulwich, B. (1997). LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data. AI Magazine (pp. 37-45).
21. Lam, S. K., Frankowski, D., & Riedl, J. (2006). Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In International Conference on Emerging Trends in Information and Communication Security.
22. Lin, W., Alvarez, S., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. In Data Mining and Knowledge Discovery, 6, 83-105.
23. Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. In IEEE Internet Computing, 7(1), 76-80.
24. McNee, S., Riedl, J., & Konstan, J. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. In Extended Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems.
25. th National Conference on Artificial Intelligence (pp. 187-192).
26. Miller, B., Albert, I., Lam, S., Konstan, J., & Riedl, J. (2003). MovieLens unplugged: experiences with an occasionally connected recommender system. In 8th international conference on Intelligent User Interfaces (pp. 263-266). ACM.
27. Mooney, R., & Roy, L. (1999). Content-based book recommending using learning for text categorization. In ACM SIGIR'99, Workshop on Recommender Systems: Algorithms and Evaluation.
28. Nageswara Rao, K., & Talwar, V.G. (2008). Application domain and functional classification of recommender systems a survey. In Desidoc journal of library and information technology, vol 28, no 3, 17-36.
29. Nguyen, A., Denos, N., & Berrut, C. (2007). Improving new user recommendations with rulebased induction on cold user data. In RecSys2007 (pp. 121-128).
30. O'Conner, M., & Herlocker, J. (1999). Clustering items for collaborative filtering. In ACM SIGIR Workshop on Recommender Systems.
31. Paterek A. (2007). Improving regularized singular value decomposition for collaborative filtering. In KDD cup Workshop at SIGKDD.
32. Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. In Machine Learning, 27, 313-331.
33. Pazzani, M., & Billsus, D. (2007). Content-Based Recommendation Systems. In The Adaptive Web, 325-341.
34. Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. In Artificial Intelligence Review, 13(5-6), 393-408.
35. Pennock, D., Horvitz, E., Lawrence, S., & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. In 16th Conference on Uncertainty in Artificial Intelligence (pp. 473-480).
36. Polcicova, G., Slovak, R., & Navrat, P. (2000). Combining content-based and collaborative filtering. In ADBIS-DASFAA Symposium (pp. 118-127).
37. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Conference on Computer Supported Cooperative Work (pp. 175-186). ACM.
38. Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In ACM Conference on Electronic Commerce (pp. 158-167).
39. Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In 10th International World Wide Web Conference.
40. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating "word of mouth". In ACM Conference on Human Factors in Computing Systems, 1, 210-217.
41. Takacs, G., Pilaszy, I., Nemeth, B., & Tikk, D. (2007). On the gravity recommendation system. In KDD cup Workshop at SIGKDD.
42. Ungar, L., & Foster, D. (1998). Clustering methods for collaborative filtering. In Workshop on Recommendation Systems. AAAI Press.
43. Vozalis, M., & Margaritis, K. G. (2004). Enhancing collaborative filtering with demographic data: The case of item-based filtering. In 4th International Conference on Intelligent Systems Design and Applications (pp. 361-366).
44. th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 501-508).
45. Zhang, Y., Callan, J., & Minka, T. (2002). Novelty and redundancy detection in adaptive filtering. In ACM SIGIR'02.
46. Ziegler, C.-N., McNee, S., Konstan, J., & Lausen, G. (2005). Improving recommendation lists through topic diversification. In 14th International World Wide Web Conference (pp. 22-32).