Bounded matrix factorization for recommender system

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 f...

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Bibliographic Details
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
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Summary: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.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-013-0710-2