Aspect-aware Asymmetric Representation Learning Network for Review-based Recommendation

Recently, user-provided reviews have been identified as an essential resource to improve user and item representation in recommender systems. Previous methods focus on the review-based recommender typically leverages symmetric networks to process user and item reviews. However, in reality, these two sets of reviews are markedly different: a user's reviews reflect the experience of buying diverse items and show their heterogeneous interests. In contrast, an item's reviews emphasize the quality of the specific item. Thus an item's reviews are usually homogeneous. This paper seeks to explore the aspect of review difference in the review-based recommendation framework. We propose a novel asymmetric neural network model that accurately learns the user and item representation by identifying this critical difference. We focus on capturing the dynamic change of user interest for the user-aspect reviews via modeling the temporal information into the conventional neural network(CNN). On the other side, we try to identify a specific item's essential yet essential features by utilizing the self-attention neural network. Finally, a factorization machine (FM) is adopted to finish the rating prediction task, where the user and item IDs are encoded as supplementary review embedding. We conduct comprehensive experiments on four Amazon datasets, and the experimental results show that our proposed model consistently outperforms several state-of-the-art methods.