32 Scopus citations

Abstract

Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.

Original languageEnglish (US)
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages1-8
Number of pages8
DOIs
StatePublished - Nov 20 2013
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
CountryChina
CityHong Kong
Period10/12/1310/16/13

Keywords

  • Content context
  • Review rating prediction
  • Review recommendation
  • Social context

ASJC Scopus subject areas

  • Software

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  • Cite this

    Tang, J., Gao, H., Hu, X., & Liu, H. (2013). Context-aware review helpfulness rating prediction. In RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems (pp. 1-8). (RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems). https://doi.org/10.1145/2507157.2507183