Hybrid Neural Recommendation with Joint Deep Representation Learning of Ratings and Reviews

Publication date: Available online 25 September 2019Source: NeurocomputingAuthor(s): Hongtao Liu, Yian Wang, Qiyao Peng, Fangzhao Wu, Lin Gan, Lin Pan, Pengfei JiaoAbstractRating-based methods (e.g., collaborative filtering) in recommendation can explicitly model users and items from their rating patterns, nevertheless suffer from the natural data sparsity problem. In other hand, user-generated reviews can provide rich semantic information of user preference and item features, and can alleviate the sparsity problems of rating data. In fact, ratings and reviews are complementary and can be viewed as two different sides of users and items, hence fusing rating patterns and text reviews effectively has the potential to learn more accurate representations of users and items for recommendation. In this paper, we propose a hybrid neural recommendation model to learn the deep representations for users and items from both ratings and reviews. Our model contains three major components, i.e., a rating-based encoder to learn deep and explicit features from rating patterns of users and items, a review-based encoder to model users and items from text reviews, and the prediction module for recommendation according to the rating- and review-based representations of users and items. In addition, considering that different reviews have different informativeness for modelling users and items, we introduce a novel review-level attention mechanism incorporating with rating-based representation as...
Source: Neurocomputing - Category: Neuroscience Source Type: research