Journal
INFORMATION SCIENCES
Volume 372, Issue -, Pages 276-293Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.08.042
Keywords
Text modeling; Recommender system; Dimension reduction; Tensor decomposition; Topic model
Categories
Funding
- National Natural Science Foundation of China [61379034, U1509221]
- National Key Technology RD Program [2014BAH28F05, 2015BAH07F01]
- Guangdong Province Science and Technology Program [2013B040100004, 2013B040403002]
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Reviews are collaboratively generated by users on items and generally contain rich information than ratings in a recommender system scenario. Ratings are modeled successfully with latent space models by capturing interaction between users and items. However, only a few models collaboratively deal with documents such as reviews. In this study, by modeling reviews as a three-order tensor, we propose a refined tensor topic model (TTM) for text tensors inspired by Tucker decomposition. User and item dimensions are co-reduced with vocabulary space, and interactions between users and items are captured using a core tensor in dimension-reduced form. TTM is proposed to obtain low-rank representations of words as well as of users and items. Furthermore, general rules are developed to transform a decomposition model into a probabilistic model. TTM is augmented further to predict ratings with the assistance of a low-dimensional representation of users and items obtained, by TTM. This augmented model is called matrix factorization by learning a bilinear map. A core regularized version is further developed to incorporate additional information from the TTM. Encouraging experimental results not only show that the TTM outperforms existing topic models in modeling texts with a user-item-word structure, but also show that our proposed rating prediction models outperform state-of-the-art approaches. (C) 2016 Elsevier Inc. All rights reserved.
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