Songyin Luo
ABSTRACT
Two-tower neural networks are popularly used in review-aware recommender systems, in which two encoders are separately employed to learn representations for users and items from reviews. However, such an architecture isolates the information exchange between two encoders, resulting in suboptimal recommendation accuracy. To this end, we propose a novel two-tower style Neural Recommendation with Cross-modality Mutual Attention (NRCMA), which bridges user encoder and item encoder crossing reviews and ratings, in order to select informative words and reviews to learn better representation for users and items. Extensive experiments on three benchmark datasets demonstrate that the cross-modality mutual attention is beneficial to two-tower neural networks, and NRCMA consistently outperforms state-of-the-art review-aware item recommendation techniques.
Supplemental Material
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Index Terms
- Review-Aware Neural Recommendation with Cross-Modality Mutual Attention
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