Leveraging prior ratings for recommender systems in e-commerce

Guibing Guo, Jie Zhang, Daniel Thalmann, Neil Yorke-Smith

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)

Abstract

User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance.

Original languageEnglish
Pages (from-to)440-455
Number of pages16
JournalElectronic Commerce Research and Applications
Volume13
Issue number6
DOIs
Publication statusPublished - 1 Nov 2014
Externally publishedYes

Keywords

  • Cold start
  • Data sparsity
  • Prior ratings
  • Rating confidence
  • Recommender systems
  • Similarity measure

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