Understanding and predicting users’ rating behavior: A cognitive perspective

Qiudan Li, Daniel Dajun Zeng, David Jingjun Xu, Ruoran Liu, Riheng Yao

Research output: Contribution to journalArticlepeer-review

Abstract

Online reviews are playing an increasingly important role in understanding and predicting users’ rating behavior, which brings great opportunities for users and organizations to make better decisions. In recent years, rating prediction has become a research hotspot. Existing research primarily focuses on generating content representation based on context information and using the overall rating score to optimize the semantics of the content, which largely ignores aspect ratings reflecting users’ feelings about more specific attributes of a product and semantic associations among aspect ratings, words, and sentences. Cognitive theory research has shown that users evaluate and rate products following the part–whole pattern; namely, they use aspect ratings to explicitly express sentiments toward aspect attributes of products and then describe those attributes in detail through the corresponding opinion words and sentences. In this paper, we develop a deep learning-based method for understanding and predicting users’ rating behavior, which adopts the hierarchical attention mechanism to unify the explicit aspect ratings and review contents. We conducted experiments using data collected from two real-world review sites and found that our proposed approach significantly outperforms existing methods. Experiments also show that the performance advantage of the proposed approach mainly comes from the high-quality representation of review content and the effective integration of aspect ratings. A user study empirically shows that aspect ratings influence users’ perceived review helpfulness and reduce users’ cognitive effort in understanding the overall score given for a product. The research contributes to the rating behavior analysis literature and has significant practical implications.

Original languageEnglish (US)
Pages (from-to)996-1011
Number of pages16
JournalINFORMS Journal on Computing
Volume32
Issue number4
DOIs
StatePublished - Sep 2020
Externally publishedYes

Keywords

  • Aspect rating
  • Cognitive theory
  • Rating behavior analysis
  • Rating prediction
  • Review content

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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