Probabilistic Local Matrix Factorization Based on User Reviews
Local matrix factorization (LMF) methods have been shown to yield competitive performance in rating prediction. The main idea is to leverage the ensemble of submatrices for better low-rank approximation. However, the generated submatrices and recommendation results in the existing methods are usuall...
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Published in | Information Retrieval Technology pp. 154 - 166 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Online Access | Get full text |
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Summary: | Local matrix factorization (LMF) methods have been shown to yield competitive performance in rating prediction. The main idea is to leverage the ensemble of submatrices for better low-rank approximation. However, the generated submatrices and recommendation results in the existing methods are usually hard to interpret. To address this issue, we adopt a probabilistic approach to enhance model interpretability of LMF methods by leveraging user reviews. In specific, we incorporate item-topics to construct meaningful “local clusters”, and further associate them with opinionated word-topics to explain the corresponding semantics and sentiments of users’ ratings. The proposed approach is a joint model which characterizes both ratings and review text. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed model compared with several state-of-art methods. More importantly, the produced results provide meaningful explanations to understand users’ ratings and sentiments. |
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ISBN: | 3319701444 9783319701448 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-319-70145-5_12 |