Collaborative filtering with information-rich and information-sparse entities

Kai Zhu, Rui Wu, Lei Ying, R. Srikant

Research output: Contribution to journalArticle

Abstract

In this paper, we consider a popular model for collaborative filtering in recommender systems. In particular, we consider both the clustering model, where only users (or items) are clustered, and the co-clustering model, where both users and items are clustered, and further, we assume that some users rate many items (information-rich users) and some users rate only a few items (information-sparse users). When users (or items) are clustered, our algorithm can recover the rating matrix with ω (M K log M) noisy entries while M K entries are necessary, where K is the number of clusters and M is the number of items. In the case of co-clustering, we prove that K2 entries are necessary for recovering the rating matrix, and our algorithm achieves this lower bound within a logarithmic factor when K is sufficiently large. Extensive simulations on Netflix and MovieLens data show that our algorithm outperforms the alternating minimization and the popularity-among-friends algorithm. The performance difference increases even more when noise is added to the datasets.

Original languageEnglish (US)
Pages (from-to)177-203
Number of pages27
JournalMachine Learning
Volume97
Issue number1-2
DOIs
StatePublished - Oct 2014

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Keywords

  • Clustering model
  • Collaborative filtering
  • Matrix completion
  • Recommender system

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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