Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
This work addresses personalized rating prediction for users and items in recommendation systems, but it appears incremental as it builds on existing attention and latent factor methods.
The paper tackled the problem of rating prediction in recommendation systems by proposing a hierarchical attention model that fuses latent factor models to focus on important words and informative reviews, with experiments on real-world datasets validating its effectiveness.
Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have different informativeness for users or items. Besides, different users and items should be personalized. Most existing works regard all reviews equally or utilize a general attention mechanism. In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews. Specially, we use the factor vectors of Latent Factor Model to guide the attention network and combine the factor vectors with feature representation learned from reviews to predict the final ratings. Experiments on real-world datasets validate the effectiveness of our approach.