Collaborative ranking for local preferences

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

Research output: Contribution to journalConference article

Abstract

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
Volume33
StatePublished - Jan 1 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
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Collaborative ranking for local preferences'. Together they form a unique fingerprint.

  • Cite this

    Kapicioglu, B., Rosenberg, D. S., Schapire, R. E., & Jebara, T. (2014). Collaborative ranking for local preferences. Journal of Machine Learning Research, 33, 466-474.