Unifying nearest neighbors collaborative filtering

RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems

Volumn , Issue , 2014, Pages 177-184

  Verstrepen, Koen ^a   Goethals, Bart ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

Explaining recommendations; Nearest neigh bors; One class collaborative filtering; Recommender systems; Top N recommendation

Indexed keywords

PURCHASING; RECOMMENDER SYSTEMS;

EXPLAINING RECOMMENDATIONS; ITEM-BASED; ITEM-BASED ALGORITHMS; NEAREST NEIGHBORS; NEAREST NEIGHBORS METHOD; NOVEL ALGORITHM; STATE-OF-THE-ART ALGORITHMS; TOP-N RECOMMENDATION;

COLLABORATIVE FILTERING;

EID: 84908884033     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2645710.2645731     Document Type: Conference Paper

References (19)

1. F. Aiolli. Efficient top-n recommendation for very large scale binary rated datasets. In RecSys, pages 273-280, 2013.
2. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. TOIS, 22(1):143-177, 2004.
3. C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In F. Ricci, L. Rokach, B. Shapira, and P. Kantor, editors, Recommender Systems Handbook. Springer, Boston, MA, 2011.
4. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In RecSys, pages 305-308, 2011.
5. Grouplens. ml-1m.zip. http://grouplens.org/datasets/movielens/.
6. J. Herlocker, J. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In CSCW, pages 241-250, 2000.
7. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, pages 263-272, 2008.
8. S. Kabbur, X. Ning, and G. Karypis. Fism: Factored item similarity models for top-n recommender systems. In KDD, pages 659-667, 2013.
9. X. Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. In ICDM, pages 497-506, 2011.
10. R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In ICDM, pages 502-511, 2008.
11. B. Pradel, N. Usunier, and P. Gallinari. Ranking with non-random missing ratings: Inuence of popularity and positivity on evaluation metrics. In RecSys, pages 147-154, 2012.
12. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452-461, 2009.
13. B. Sarwar, G. Karypis, J. Kostan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In EC, pages 158-167, 2000.
14. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: Learning to maximize reciprocal rank with collaborative less-is-more filtering. In RecSys, pages 139-146, 2012.
15. B. Sigurbjornsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In WWW, pages 327-336, 2008.
16. P. Symeonidis, A. Nanopoulos, A. Papadopoulos, and Y. Manolopoulos. Nearest-biclusters collaborative filtering based on constant and coherent values. Inf. Retr., 11(1):51-75, 2008.
17. N. Tintarev. Explanations of recommendations. In RecSys, pages 203-206, 2007.
18. J. Wang, A. P. de Vries, and M. J. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR, pages 501-508, 2006.
19. Yahoo!Research. Yahoo webscope r3.tgz. http://research.yahoo.com/Academic Relations.