The Effect of Sparsity on Collaborative Filtering Metrics

Conferences in Research and Practice in Information Technology Series

Volumn 92, Issue , 2009, Pages 9-17

  Bobadilla, Jesus ^a   Serradilla, Francisco ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Collaborative filtering; Metric; Recommender systems; Sparsity

Indexed keywords

COLLABORATIVE FILTERING; CONTENT-BASED; MEAN ABSOLUTE ERROR; MEAN-SQUARED DIFFERENCES; METRIC; SPARSITY;

FORECASTING; RECOMMENDER SYSTEMS;

MATRIX ALGEBRA;

EID: 84873478895     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Knights, M. Web 2.0, IET Communications Engineer, (February-March 2007), 30-35
2. Lin, K.J., Building Web 2.0, Computer, (May 2007), 101-102
3. Janner, T., Schroth, C. Web 2.0 and SOA: Converging Concepts Enabling the Internet of Services, IT Pro, (May-June 2007), 36-41
4. Jinghua, H., Kangning, W., Shaohong, F. "A Survey of E-Commerce Recommender Systems", in Proceedings of the International Conference on Service Systems and Service Management, 2007, 10.1109/ICSSSM.2007.4280214, 1-5
5. Baraglia, R., Silvestri, F. "An Online Recommender System for Large Web Sites", in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 2004, 10.1109/WI.2004.10158, 199-205
6. Fesenmaier, D.R., Gretzel, U., Knoblock, C., Paris, C., Ricci, F., Stabb, S., Werther, H., Zipf, A. Intelligent Systems for Tourism, Intelligent Systems, vol. 17, no. 6, (nov/dec 2002), 53-66
7. Konstan, J.A., Miller, B.N., Riedl, J. PocketLens: Toward a Personal Recommender System, ACM Transactions on Information Systems, vol. 22, no. 3, (July 2004), 437-476.
8. Antonopoulus, N., Salter, J., CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering, IEEE Intelligent Systems, (January/February 2006), 35-41
9. Li, P., Yamada, S. "A movie recommender system based on inductive learning" in Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, 318-323
10. Adomavicius, Tuzhilin, A. Toward the Next Generation of Recommender Systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Enginnering, vol. 17, no. 6, (June 2005), 734-749
11. Herlocker, J. L., Konstan, J.A., Riedl, J.T., Terveen, L.G. Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, vol. 22, no. 1, (January 2004), 5-53
12. Breese, J.S., Heckerman, D., Kadie, C. "Empirical Analysis of Predictive Algorithms for Collaborative Filtering", in Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 1998, 43-52
13. Kong, F., Sun, X., Ye, S. A Comparison of Several Algorithms for Collaborative Filtering in Startup Stage, In Proceedings of the IEEE networking, sensing and control, (March 2005), 25-28
14. Cho, S.B., Hong, J.H., Park, M.H. Location-Based Recommendation System Using Bayesian User?s Preference Model in Mobile Devices, Lecture Notes on Computer Science, vol. 4611, (August 2007), 1130-1139
15. Ingoo, H., Kyong, J.O., Tae, H.R. The Collaborative Filtering Recommendation Based on SOM Cluster-Indexing CBR, Expert Systems with Applications, vol. 25, 2003, 413-423
16. Yager, R.R. Fuzzy Logic Methods in Recommender Systems, Fuzzy Sets and Systems, vol. 136, no. 2, (June 2003), 133-149
17. Linden, G., Smith, B., York, J., Amazon.com Recommendations, IEEE Internet Computing, (January-February 2003), 76-80
18. Giaglis, G.M., Lekakos, Improving the Prediction Accuracy of Recommendation Algorithms: Approaches Anchored on Human Factors, Interacting with Computers, vol. 18, no. 3, 2006, 410-431
19. Li, Y., Nayak, R., Weng, L.T., Xu, Y., "An Improvement to Collaborative Filtering for Recommender Systems", in Proceedings of the IEEE International Conference on Computational Intelligence for Modelling, Control and Automatitation, 2005
20. Fuyuki, I., Quan, T.K., Shinichi, H., "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
21. Manolopoulus, Y., Nanopoulus A., Papadopoulus A.N., Symeonidis P. Collaborative Recommender Systems: Combining Effectiveness and Efficiency, Expert Systems with Applications, 2007. in press.
22. Herlocker, J., Konstan J., Riedl, J. An empirical Analysis of Design Choices in Neighborhood-based Collaborative Filtering Algorithms, Information Retrieval, vol.5, no. 4,, 2002, 287-310.
23. Pazzani, M. A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Rev., December 1999, 393-408.
24. Sarwar, B., Karypis, G., Konstan J., Riedl, J. Application of Dimensionality Reduction in Recommender Systems-A Case Study, in Proceedings of ACM WebKDD Workshop, 2000.
25. Goldbergh, K., Roeder, T., Gupta D., Perkins D., Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, Vol. 4, 2001, 133-151
26. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer T.K., Harshman, R., Indexing by Latent Semantic Analysis, in Proceedings of the JASIS, Vol 41(6), 1990.
27. Hofmann, T., Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis, in Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, 2003.
28. Papagelis, M., Plexousakis D. Kutsuras, T., Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences, Lectures Notes on Computer Science, Vol. 3477, 2005, 224-239.
29. Bruyn, A.D., Giles C.L., Pennock, D.M., Offering Collaborative-Like Recommendations when Data is Sparse: the Case of Attraction-Weighted Information Filtering, in Proceedings of Lecture Notes in Computer Science, Vol. 3137, 2004, 393-396.
30. Polcicova G., Peter Tino, Making Sense of Sparse Rating Data in Collaborative Filtering via Topographic Organization of User Preference Patterns, Neural Networks, Vol. 17, 2004, 1183-1199.
31. http://www.movielens.org.