  Compliments and concerns in reviews are valuable for understanding users'
shopping interests and their opinions with respect to specific aspects of
certain items. Existing review-based recommenders favor large and complex
language encoders that can only learn latent and uninterpretable text
representations. They lack explicit user attention and item property modeling,
which however could provide valuable information beyond the ability to
recommend items. Therefore, we propose a tightly coupled two-stage approach,
including an Aspect-Sentiment Pair Extractor (ASPE) and an
Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines
Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as
concrete aspect-level evidence. Extensive experiments on seven real-world
Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs
which enable APRE to deliver superior accuracy over the leading baselines.
