Bounded matrix factorization for recommender system

• Published in: Knowledge and information systems 2014-06, Vol.39 (3), p.491-511
• Main Authors: Kannan, Ramakrishnan, Ishteva, Mariya, Park, Haesun
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Approximation; Artificial intelligence; Collaboration; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data mining; Data Mining and Knowledge Discovery; Database Management; Dating techniques; Descent; Exact sciences and technology; Factorization; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Learning and adaptive systems; Least squares method; Machine learning; Mathematical models; Principal components analysis; Ratings; Ratings & rankings; Recommender systems; Regular Paper; Semantics; Software; Studies
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-013-0710-2

Matrix factorization has been widely utilized as a latent factor model for solving the recommender system problem using collaborative filtering. For a recommender system, all the ratings in the rating matrix are bounded within a pre-determined range. In this paper, we propose a new improved matrix factorization approach for such a rating matrix, called Bounded Matrix Factorization (BMF), which imposes a lower and an upper bound on every estimated missing element of the rating matrix. We present an efficient algorithm to solve BMF based on the block coordinate descent method. We show that our algorithm is scalable for large matrices with missing elements on multicore systems with low memory. We present substantial experimental results illustrating that the proposed method outperforms the state of the art algorithms for recommender system such as stochastic gradient descent, alternating least squares with regularization, SVD++ and Bias-SVD on real-world datasets such as Jester, Movielens, Book crossing, Online dating and Netflix.