Question-Attentive Review-Level for Neural Rating Regression

Code

• github.com/PreferredAI/QuestER
  tf★ 0

Abstract

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments on datasets of ten product categories showcase the efficacies of our method as compared to comparable baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.

Tasks

Reproductions