Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2022

Abstract

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on 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 showcase the efficacies of our method as compared to baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.

Keywords

Planets, Big Data, Task analysis

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, December 17-20

First Page

1

Last Page

6

ISBN

9781665480468

Identifier

10.1109/BigData55660.2022.10020538

Publisher

IEEE

City or Country

USA

Additional URL

https://doi.org/10.1109/BigData55660.2022.10020538

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