Collaborative ranking for local preferences

Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


For many collaborative ranking tasks, we have access to relative preferences among subsets of items, but not to global preferences among all items. To address this, we introduce a matrix factorization framework called Collaborative Local Ranking (CLR). We justify CLR by proving a bound on its generalization error, the first such bound for collaborative ranking that we know of. We then derive a simple alternating minimization algorithm and prove that its running time is independent of the number of training examples. We apply CLR to a novel venue recommendation task and demonstrate that it outperforms state-of-the-art collaborative ranking methods on real-world data sets.

Original languageEnglish (US)
Pages (from-to)466-474
Number of pages9
JournalJournal of Machine Learning Research
StatePublished - 2014
Event17th International Conference on Artificial Intelligence and Statistics, AISTATS 2014 - Reykjavik, Iceland
Duration: Apr 22 2014Apr 25 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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