Using Adjective Features from User Reviews to Generate Higher Quality and Explainable Recommendations

  • Xiaoying Xu
  • Anindya Datta
  • Kaushik Dutta
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 389)


Recommender systems have played a significant role in alleviating the “information overload” problem. Existing Collaborative Filtering approaches face the data sparsity problem and transparency problem, and the content-based approaches suffer the problem of insufficient attributes. In this paper, we show that abundant adjective features embedded in user reviews can be used to characterize movies as well as users’ taste. We extend the standard TF-IDF term weighting scheme by introducing cluster frequency (CLF) to automatically extract high quality adjective features from user reviews for recommendation. We also develop a movie recommendation framework incorporating adjective features to generated highly accurate rating prediction and high quality recommendation explanation. The results of experiments performed on a real world dataset show that our proposed method outperforms the state-of-the-art techniques.


Recommender systems User reviews Adjective Features Sparsity Transparency 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Xiaoying Xu
    • 1
  • Anindya Datta
    • 1
  • Kaushik Dutta
    • 1
  1. 1.Department of Information Systems, School of ComputingNational University of SingaporeSingapore

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