A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems
This work provides an incremental improvement in review modeling for recommender systems, benefiting users by potentially offering more accurate recommendations.
This paper addresses the limitations of existing CNN and RNN-based methods for review modeling in recommender systems by proposing a hierarchical self-attentive convolution network. The model combines convolution networks with self-attention to capture both local and global text interactions, achieving significantly better performance on the Amazon Product Benchmark compared to state-of-the-art review-based recommendation models.
Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neural Network (CNN) based models that extract n-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN) based models that learn global contextual representations from reviews for users and items. Despite their success, both CNN and RNN based models in previous studies suffer from their own drawbacks. While CNN based models are weak in modeling long-dependency relation in text, RNN based models are slow in training and inference due to their incapability with parallel computing. To alleviate these problems, we propose a new text encoder module for review modeling in recommendation by combining convolution networks with self-attention networks to model local and global interactions in text together.As different words, sentences, reviews have different importance for modeling user and item representations, we construct review models hierarchically in sentence-level, review-level, and user/item level by encoding words for sentences, encoding sentences for reviews, and encoding reviews for user and item representations. Experiments on Amazon Product Benchmark show that our model can achieve significant better performance comparing to the state-of-the-art review based recommendation models.