Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2024

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 10 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.

Keywords

Recommendation explanations, Review attention, Recommendation reviews

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Computation

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

15

Issue

6

First Page

1

Last Page

25

ISSN

2157-6904

Identifier

10.1145/3699516

Publisher

Association for Computing Machinery (ACM)

Comments

PDF provided by faculty

Additional URL

https://doi.org/10.1145/3699516

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