Review-guided Helpful Answer Identification in E-commerce

Wenxuan Zhang, Wai Lam, Yang Deng, Jing Ma

Research output: Chapter in book/report/conference proceedingConference proceeding

17 Citations (Scopus)


Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.
Original languageEnglish
Title of host publicationWWW '20: Proceedings of The Web Conference 2020
EditorsYennun Huang, Irwin King, Tie-Yan Liu
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Print)9781450370233
Publication statusPublished - Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020


Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
Internet address

User-Defined Keywords

  • answer helpfulness prediction
  • question answering
  • E-commerce


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