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Using reviews to learn user and item representations is important forrecommender system. Current review based methods can be divided into twocategories: (1) the Convolution Neural Network (CNN) based models that extractn-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN)based models that learn global contextual representations from reviews forusers and items. Despite their success, both CNN and RNN based models inprevious studies suffer from their own drawbacks. While CNN based models areweak in modeling long-dependency relation in text, RNN based models are slow intraining and inference due to their incapability with parallel computing. Toalleviate these problems, we propose a new text encoder module for reviewmodeling in recommendation by combining convolution networks withself-attention networks to model local and global interactions in texttogether.As different words, sentences, reviews have different importance formodeling user and item representations, we construct review modelshierarchically in sentence-level, review-level, and user/item level by encodingwords for sentences, encoding sentences for reviews, and encoding reviews foruser and item representations. Experiments on Amazon Product Benchmark showthat our model can achieve significant better performance comparing to thestate-of-the-art review based recommendation models.

A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems