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 language | English (US) |
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Pages (from-to) | 177-203 |
Number of pages | 27 |
Journal | Machine Learning |
Volume | 97 |
Issue number | 1-2 |
DOIs | |
State | Published - Oct 2014 |
Keywords
- Clustering model
- Collaborative filtering
- Matrix completion
- Recommender system
ASJC Scopus subject areas
- Software
- Artificial Intelligence