Generating virtual ratings from chinese reviews to augment online recommendations

Weishi Zhang*, Guiguang Ding, Li CHEN, Chunping Li, Chengbo Zhang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

74 Citations (Scopus)


Collaborative filtering (CF) recommenders based on User-Item rating matrix as explicitly obtained from end users have recently appeared promising in recommender systems. However, User-Item rating matrix is not always available or very sparse in some web applications, which has critical impact to the application of CF recommenders. In this article we aim to enhance the online recommender system by fusing virtual ratings as derived from user reviews. Specifically, taking into account of Chinese reviews' characteristics, we propose to fuse the self-supervised emotion-integrated sentiment classification results into CF recommenders, by which the User-Item Rating Matrix can be inferred by decomposing item reviews that users gave to the items. The main advantage of this approach is that it can extend CF recommenders to some web applications without user rating information. In the experiments, we have first identified the self-supervised sentiment classification's higher precision and recall by comparing it with traditional classification methods .Furthermore, the classification results, as behaving as virtual ratings, were incorporated into both user-based and item-based CF algorithms. We have also conducted an experiment to evaluate the proximity between the virtual and real ratings and clarified the effectiveness of the virtual ratings. The experimental results demonstrated the significant impact of virtual ratings on increasing system's recommendation accuracy in different data conditions (i.e., conditions with real ratings and without).

Original languageEnglish
Article number9
JournalACM Transactions on Intelligent Systems and Technology
Issue number1
Publication statusPublished - Jan 2013

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence

User-Defined Keywords

  • Information retrieval
  • Online recommendation
  • Sentiment analysis


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