A belief propagation based recommender system for online services

RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems

Volumn , Issue , 2010, Pages 217-220

  Ayday, Erman ^a   Fekri, Faramarz ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Belief propagation; E commerce; Iterative algorithms; Online services; Recommender systems

Indexed keywords

BELIEF PROPAGATION; E-COMMERCE; ITERATIVE ALGORITHM; ON-LINE SERVICE; RECOMMENDER SYSTEMS;

ALGORITHMS; CODING ERRORS; COMPUTER SIMULATION; ELECTRONIC COMMERCE; FREQUENCY SELECTIVE FADING; GRAPH THEORY; ITERATIVE DECODING; MESSAGE PASSING;

ONLINE SYSTEMS;

EID: 78649921773     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1864708.1864751     Document Type: Conference Paper

References (11)

1. E. Ayday, H. Lee, and F. Fekri. An iterative algorithm for trust and reputation management. ISIT '09 Proceedings of IEEE International Symposium on Information Theory, 2009.
2. M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40:66-72, 1997.
3. 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, pages 43-52, 1998.
4. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133-151, 2001.
5. 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.
6. T. Hofmann. Latent class models for collaborative filtering. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pages 688-693, 1999.
7. F. Kschischang, B. Frey, and H. A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47:498-519, Feb. 2001.
8. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6(1):61-82, 2002.
9. H. Pishro-Nik and F. Fekri. On decoding of low-density parity-check codes on the binary erasure channel. IEEE Transactions on Information Theory, 50:439-454, March 2004.
10. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system - a case study. 2000.
11. G. Shafer. The Dempster-Shafer theory. Encyclopedia of Artificial Intelligence, 1992.