Local collaborative ranking

Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

109 Scopus citations


Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low- rank. In this paper, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state- of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish (US)
Title of host publicationWWW 2014 - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450327442
StatePublished - Apr 7 2014
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: Apr 7 2014Apr 11 2014

Publication series

NameWWW 2014 - Proceedings of the 23rd International Conference on World Wide Web


Other23rd International Conference on World Wide Web, WWW 2014
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


  • Collaborative filtering
  • Ranking
  • Recommender systems


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