Trust-based collaborative filtering

IFIP International Federation for Information Processing

Volumn 263, Issue , 2008, Pages 119-134

  Lathia, Neal ^a   Hailes, Stephen ^a   Capra, Licia ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION FILTERING; LEARNING ALGORITHMS; NEAREST NEIGHBOR SEARCH; RECOMMENDER SYSTEMS;

INFORMATION OVERLOADS; K NEAREST NEIGHBOURS (K-NN); PREDICTION ACCURACY; RATING INFORMATION; SIMILARITY-BASED METHODS;

COLLABORATIVE FILTERING;

EID: 44149114235     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-0-387-09428-1_8     Document Type: Conference Paper

References (20)

1. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. In ACM Transactions on Information Systems, volume 22, pages 5-53. ACM Press, 2004.
2. M. Carbone, M. Nielsen, and V. Sassone. A formal model for trust in dynamic networks. In In Proceedings of Int. Conference on Software Engineering and Formal Methods, (SEFM), 2003.
3. Daniele Quercia, Manish Lad, Stephen Hailes, Licia Capra, and Saleem Bhatti. Strudel: Supporting trust in the dynamic establishment of peering coalitions. In Proceedings of the 21st ACM Symposium on Applied Computing, pages 1870-1874, Dijon, France, April 2006.
4. L. Yan, S. Hailes, and L. Capra. Analysis of packet relaying models and incentive strategies in wireless ad hoc networks with game theory. In Proc. IEEE 22nd International Conference on Advanced Information Networking and Applications (AINA08), GinoWan, Okinawa, Japan, March 2008. IEE Computer Society.
5. J.B. Schafer, J. Konstan, and J. Riedl. Recommender systems in e-commerce. In Proceedings of the ACM Conference on Electronic Commerce, 1999.
6. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An Algorithmic Framework for Performing Collaborative Filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230-237, 1999.
7. G. Linden, B. Smith, and Y. York. Amazon.com recommendations: Item-to-item collaborative filtering. In IEEE Internet Computing, pages 76-80, 2003.
8. N. Lathia, S. Hailes, and L. Capra. The effect of correlation coefficients on communities of recommenders. In To Appear in ACM SAC TRECK, 2008.
9. R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proceedings of the 13th international conference on World Wide Web, pages 403-412, 2004.
10. K. Crammer and Y. Singer. Pranking with ranking. In Proceedings of the Conference on Neural Information Processing Systems (NIPS), 2001.
11. R. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In IEEE International Conference on Data Mining (ICDM'07). IEEE, 2007.
12. A. Abdul-Rahman and S. Hailes. Supporting trust in virtual communities. In Proceedings of the 33rd International Conference on System Sciences, Hawaii, USA, 2000.
13. A. Agresti and L. Winner. Evaluating agreement and disagreement among movie reviewers. In Chance, volume 10, 1997.
14. J. O'Donovan and B. Smyth. Trust in recommender systems. In IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces, pages 167-174. ACM Press, 2005.
15. I. Im and A. Hars. Does a one-size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes. In ACM Transactions on Information Systems (TOIS), volume 26, November 2007.
16. P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of Recommender Systems (RecSys), 2007.
17. D. Quercia, S. Hailes, and L. Capra. Lightweight distributed trust propagation. In Proceedings of the 7th IEEE International Conference on Data Mining, Omaha, US, October 2007.
18. G. Pitsilis and L. Marshall. A trust-enabled p2p recommender system. In Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pages 59-64. IEEE, 2006.
19. G. Pitsilis and L. Marshall. Trust as a Key to Improving Recommendation Systems, Trust Management, pages 210-223. Springer Berlin/Heidelberg, 2005.
20. H. Geng, X. Deng, and H. Ali. A new clustering algorithm using message passing and its applications in analyzing microarray data. In Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005.