Helpfulness prediction for online reviews with explicit content-rating interaction

Jiahua Du, Jackie Rong, Hua Wang, Yanchun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Automatic helpfulness prediction aims to prioritize online product reviews by quality. Existing methods have combined review content and star ratings for automatic helpfulness prediction. However, the relationship between review content and star ratings is not explicitly captured, which limits the capability of rating information in influencing review content. This paper proposes a deep neural architecture to learn the explicit content-rating interaction (ECRI) for automatic helpfulness prediction. Specifically, ECRI explores two methods to interact review content with star ratings and adaptively specify the amount of rating information needed by review content. ECRI is evaluated against state-of-the-art methods on six real-world domains of the Amazon 5-core dataset. Experimental results demonstrate that exploiting the explicit content-rating interaction improves automatic helpfulness prediction. The source code of ECRI can be obtained from https://github.com/tokawah/ECRI
Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2019
Subtitle of host publication20th International Conference Hong Kong, China, November 26–30, 2019 Proceedings
EditorsReynold Cheng, Nikos Mamoulis, Yizhou Sun, Xin Huang
Place of PublicationCham Switzerland
PublisherSpringer
Pages795-809
Number of pages15
ISBN (Electronic)9783030342234
ISBN (Print)9783030342227
DOIs
Publication statusPublished - 2019
EventInternational Conference on Web Information Systems Engineering 2019 - Hong Kong, Macao
Duration: 22 Jan 202022 Jan 2020
https://wise2019.comp.polyu.edu.hk/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11881
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Web Information Systems Engineering 2019
Abbreviated titleWISE 2019
CountryMacao
CityHong Kong
Period22/01/2022/01/20
Internet address

Keywords

  • E-commerce
  • Review helpfulness
  • Explicit content-rating interaction
  • Deep learning

Cite this

Du, J., Rong, J., Wang, H., & Zhang, Y. (2019). Helpfulness prediction for online reviews with explicit content-rating interaction. In R. Cheng, N. Mamoulis, Y. Sun, & X. Huang (Eds.), Web Information Systems Engineering – WISE 2019: 20th International Conference Hong Kong, China, November 26–30, 2019 Proceedings (pp. 795-809). (Lecture Notes in Computer Science; Vol. 11881). Springer. https://doi.org/10.1007/978-3-030-34223-4_50