Abstract
The problem of personalized review-based rating prediction aims at inferring users’ ratings over their unrated items using historical ratings and reviews. Most of existing methods solve this problem by integrating latent factor model and topic model to learn interpretable user and items factors. However, these methods ignore the word order in reviews and the learned topic factors are limited to review text, which cannot fully reveal the complicated interaction relations between reviews and ratings. Moreover, they only utilize overall ratings instead of multi-criteria ratings which can represent more detailed preferences of users. In this paper, we propose a deep learning framework named NRPMT in which multi-criteria ratings and user reviews can complement each other to improve recommendation accuracy. The proposed model can simultaneously predict accurate ratings and generate review content expressing user experience and feelings. For rating prediction, a neural factorization machines-based regression model are used to project the feature interactions between user, item and criteria into rating. For review generation, gated recurrent neural networks are employed to “translate” the feature representation of user, item and criteria into a review. Extensive experiments on three real-world datasets demonstrate that NRPMT achieves significant improvement over the several competitive baselines.
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This work was financially supported by the National Natural Science Foundation of China (Grant: 61502350) and Joint Funds of National Natural Science foundation of China (Grant: U1536114).
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Ding, Y., Li, S., Yu, W. et al. A unified neural model for review-based rating prediction by leveraging multi-criteria ratings and review text. Cluster Comput 22 (Suppl 4), 9177–9185 (2019). https://doi.org/10.1007/s10586-018-2098-y
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DOI: https://doi.org/10.1007/s10586-018-2098-y