Computer Science – Information Theory
Scientific paper
2010-07-03
Computer Science
Information Theory
To appear in Proc. 6th International Symposium on Turbo Codes and Iterative Information Processing, Brest, France, September 6
Scientific paper
A new message-passing (MP) method is considered for the matrix completion problem associated with recommender systems. We attack the problem using a (generative) factor graph model that is related to a probabilistic low-rank matrix factorization. Based on the model, we propose a new algorithm, termed IMP, for the recovery of a data matrix from incomplete observations. The algorithm is based on a clustering followed by inference via MP (IMP). The algorithm is compared with a number of other matrix completion algorithms on real collaborative filtering (e.g., Netflix) data matrices. Our results show that, while many methods perform similarly with a large number of revealed entries, the IMP algorithm outperforms all others when the fraction of observed entries is small. This is helpful because it reduces the well-known cold-start problem associated with collaborative filtering (CF) systems in practice.
Kim Byung-Hak
Pfister Henry D.
Yedla Arvind
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