Hydra: A hybrid recommender system - [Cross-linked rating and content information]

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 75-80

  Spiegel, Stephan ^a   Kunegis, Jérôme ^a   Li, Fang ^b  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Cross linked data; Hybrid recommender; Matrix factorization

Indexed keywords

CONTENT BASED FILTERING; CONTENT INFORMATION; HYBRID MODEL; HYBRID RECOMMENDER SYSTEMS; MATRIX FACTORIZATIONS; PREDICTION ACCURACY; PREDICTION ALGORITHMS; RECOMMENDER SYSTEMS; RUNTIMES;

ALGORITHMS; COBALT COMPOUNDS; CONTENT BASED RETRIEVAL; FACTORIZATION; KNOWLEDGE MANAGEMENT; LIGHT MEASUREMENT; MATHEMATICAL MODELS;

SINGULAR VALUE DECOMPOSITION;

EID: 74049149911     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1651274.1651289     Document Type: Conference Paper

References (13)

1. Adomavicius and Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEETKDE: IEEE Transactions on Knowledge and Data Engineering, 17, 2005.
2. R. M. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM '07: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, pages 43-52, Washington, DC, USA, 2007. IEEE Computer Society.
3. R. D. Burke. Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web, Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, pages 377-408. Springer, 2007.
4. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434, New York, NY, USA, 2008. ACM.
5. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 2003.
6. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In ACM SIGIR Workshop on Recommender Systems, pages 187-192, 2002.
7. M. J. Pazzani and D. Billsus. Content-based recommendation systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, chapter 10, pages 325-341. Springer-Verlag, Berlin, Germany, May 2007.
8. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In WWW '01: Proceedings of the 10th international conference on World Wide Web, pages 285-295, New York, NY, USA, 2001. ACM.
9. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system - a case study. In In ACM WebKDD Workshop, 2000.
10. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, chapter 9, pages 291-324. Springer-Verlag, Berlin, Germany, may 2007.
11. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: Large scale online bayesian recommendations. In WWW '09: Proceedings of the 18th international conference on World wide web, pages 111-120, New York, NY, USA, 2009. ACM.
12. A. Toescher, M. Jahrer, and R. Legenstein. Improved neighborhood-based algorithms for large-scale recommender systems. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, Graz, Austria, 2008. ACM.
13. J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501-508, New York, NY, USA, 2006. ACM Press.