  User and item reviews are valuable for the construction of recommender
systems. In general, existing review-based methods for recommendation can be
broadly categorized into two groups: the siamese models that build static user
and item representations from their reviews respectively, and the
interaction-based models that encode user and item dynamically according to the
similarity or relationships of their reviews. Although the interaction-based
models have more model capacity and fit human purchasing behavior better,
several problematic model designs and assumptions of the existing
interaction-based models lead to its suboptimal performance compared to
existing siamese models. In this paper, we identify three problems of the
existing interaction-based recommendation models and propose a couple of
solutions as well as a new interaction-based model to incorporate review data
for rating prediction. Our model implements a relevance matching model with
regularized training losses to discover user relevant information from long
item reviews, and it also adapts a zero attention strategy to dynamically
balance the item-dependent and item-independent information extracted from user
reviews. Empirical experiments and case studies on Amazon Product Benchmark
datasets show that our model can extract effective and interpretable user/item
representations from their reviews and outperforms multiple types of
state-of-the-art review-based recommendation models.
