A new collaborative filtering metric that improves the behavior of recommender systems

Knowledge-Based Systems

Volumn 23, Issue 6, 2010, Pages 520-528

  Bobadilla, J ^a   Serradilla, F ^a   Bernal, J ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Collaborative filtering; Jaccard; Mean squared differences; Metric; Recommender systems; Similarity

Indexed keywords

COLLABORATIVE FILTERING; MEAN-SQUARED DIFFERENCES; NUMERICAL INFORMATION; PEARSON CORRELATION; RECOMMENDER SYSTEMS; SOCIAL NETWORKS; WEB 2.0;

SIGNAL FILTERING AND PREDICTION;

EXPERIMENTS;

EID: 77955417387     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2010.03.009     Document Type: Article

References (40)

1. E. Adomavicius, and A. Tuzhilin 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 2005 734 749
2. AICU, , .
3. N. Antonopoulus, and J. Salter CinemaScreen recommender agent: combining collaborative and content-based filtering IEEE Intelligent Systems 2006 35 41
4. R. Baraglia, F. Silvestri, An online recommender system for large web sites, in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 2004, pp. 199-205, 10.1109/WI.2004.10158.
5. J. Bobadilla, F. Serradilla, and A. Hernando Collaborative filtering adapted to recommender systems of e-learning Knowledge-Based Systems 22 2009 261 265 10.1016/j.knosys.2009.01.008
6. J.S. Breese, D. Heckerman, and C. Kadie Empirical analysis of predictive algorithms for collaborative filtering Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence 1998 Morgan Kaufmann 43 52
7. L. Candillier, F. Meyer, M. Boullé, Comparing state-of-the-art collaborative filtering systems, in: LNAI 4571, 2007, pp. 548-562.
8. S.B. Cho, J.H. Hong, and M.H. Park Location-based recommendation system using bayesian user's preference model in mobile devices Lecture Notes on Computer Science 4611 2007 1130 1139
9. F. Hernández, and E. Gaudioso Evaluation of recommender systems: a new approach Expert Systems with Applications 35 2008 790 804 10.1016/j.eswa.2007.07.047
10. D.R. Fesenmaier, U. Gretzel, C. Knoblock, C. Paris, F. Ricci, and S. Stabb Intelligent systems for tourism Intelligent Systems 17 6 2002 53 66
11. I. Fuyuki, T.K. Quan, H. Shinichi, Improving accuracy of recommender systems by clustering items based on stability of user similarity, in: Proceedings of the IEEE International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2006, pp. 61-61, 10.1109/CIMCA.2006.123.
12. G.M. Giaglis, and G. Lekakos Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors Interacting with Computers 18 3 2006 410 431
13. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins Eigentaste: a constant time collaborative filtering algorithm Information Retrieval 4 2 2001 133 151
14. C. Hayes, and P. Cunningham Context boosting collaborative recommendations Knowledge-Based Systems 17 2-4 2004 131 138
15. J.L. Herlocker, J.A. Konstan, A.l. Borchers, J.T. Riedl, An algorithmic framework for performing collaborative filtering, in: SIGIR, 1999, pp. 230-237.
16. J.L. Herlocker, J.A. Konstan, and J.T. Riedl An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms Information Retrieval 5 2002 287 310
17. J.L. Herlocker, J.A. Konstan, J.T. Riedl, and L.G. Terveen Evaluating collaborative filtering recommender systems ACM Transactions on Information Systems 22 1 2004 5 53
18. H. Ingoo, J.O. Kyong, and H.R. Tae The collaborative filtering recommendation based on SOM cluster-indexing CBR Expert Systems with Applications 25 2003 413 423
19. T. Janner, C. Schroth, Web 2.0 and SOA: converging concepts enabling the internet of services, in: IT Professional, 2007, pp. 36-41.
20. H. Jinghua, W. Kangning, F. Shaohong, A survey of e-commerce recommender systems, in: Proceedings of the International Conference on Service Systems and Service Management, 2007, pp. 1-5, 10.1109/ICSSSM.2007.4280214. (Pubitemid 351362544)
21. M. Knights Web 2.0 IET Communications Engineer 2007 30 35
22. F. Kong, X. Sun, S. Ye, A comparison of several algorithms for collaborative filtering in startup stage, in: Proceedings of the IEEE Networking, Sensing and Control, 2005, pp. 25-28.
23. J.A. Konstan, B.N. Miller, and J. Riedl PocketLens: toward a personal recommender system ACM Transactions on Information Systems 22 3 2004 437 476
24. G. Koutrica, B. Bercovitz, H. Garcia, FlexRecs: expresing and combining flexible recommendations, in: SIGMOD, 2009, pp. 745-757.
25. P. Li, S. Yamada, A movie recommender system based on inductive learning, in: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, 2004, pp. 318-323, 10.1109/ICCIS.2004.1460433.
26. K.J. Lin Building Web 2.0 Computer 2007 101 102
27. G. Linden, B. Smith, and J. York Amazon.com recommendations IEEE Internet Computing January-February 2003 76 80
28. F. Loll, N. Pinkwart, Using collaborative filtering algorithms as eLearning tools, in: 42nd Hawaii International Conference on System Sciences HICSS'09, 2009, pp. 1-10.
29. Y. Manolopoulus, A. Nanopoulus, A.N. Papadopoulus, and P. Symeonidis Collaborative recommender systems: combining effectiveness and efficiency Expert Systems with Applications 34 2007 2995 3013 10.1016/j.eswa.2007.05.013
30. MovieLens, .
31. R. Nayak, L.T. Weng, Y. Xu, An improvement to collaborative filtering for recommender systems, in: Proceedings of the IEEE International Conference on Computational Intelligence for Modeling, Control and Automatisation, vol. 1, 2005, pp. 792-795, 10.1109/CIMCA.2005.1631361.
32. NetFlix, .
33. P. Pu, and L. Chen Trust-inspiring explanation interfaces for recommender systems Knowledge-Based Systems 20 6 2007 542 556
34. P.B. Ryan, and D. Bridge Collaborative recommending using formal concept analysis Knowledge-Based Systems 19 5 2006 309 315
35. J.L. Sanchez, F. Serradilla, E. Martinez, J. Bobadilla, Choice of metrics used in collaborative filtering and their impact on recommender systems, in: Proceedings of the IEEE International Conference on Digital Ecosystems and Technologies DEST, 2008, pp. 432-436, 10.1109/DEST.2008.4635147.
36. J.B. Schafer, D. Frankowski, J.L. Herlocker, S. Sen, Collaborative filtering recommender systems, LNCS 4321, 2007, pp. 291-324.
37. P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos Providing justifications in recommender systems IEEE Transactions on Systems, Man and Cybernetics, Part A 38 6 2008 1262 1272
38. W. Yuan, D. Guan, Y.K. Lee, S. Lee, and S.J. Hur Improved trust-aware recommender system using small-worldness of trust networks Knowledge-Based Systems 23 3 2010 232 238
39. R.R. Yager Fuzzy logic methods in recommender systems Fuzzy Sets and Systems 136 2 2003 133 149
40. J.M. Yang, and K.F. Li Recommendation based on rational inferences in collaborative filtering Knowledge-Based Systems 22 1 2009 105 114