Toward Comprehensive User and Item Representations via Three-tier Attention Network

Abstract

Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder to learn review features from words via a word-level attention, an aspect encoder to learn aspect features via a review-level attention, and a user/item encoder to learn the final representations of users/items via an aspect-level attention. In word- and review-level attentions, we adopt the context-aware mechanism to indicate importance of words and reviews dynamically instead of static attention weights. In addition, the attentions in the word and review levels are of multiple paradigms to learn multiple features effectively, which could indicate the diversity of user/item features. Furthermore, we propose a personalized aspect-level attention module in user/item encoder to learn the final comprehensive features. Extensive experiments are conducted and the results in rating prediction validate the effectiveness of our method.

Explainable Recommender With Geometric Information Bottleneck

“…extractive) solutions directly select representative text spans (rationales), i.e., words or sentences, from a target item's past reviews to explain the recommender behaviours. Attention-based methods are prevalent in reviewbased explainable recommenders [14], [15]. Beyond leveraging attention weights to select rationales, [27], [28] proposed different optimization objectives to refine the selection.…”