Skip to Main content Skip to Navigation
Conference papers

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

Abstract : 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.
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, April 28, 2017 - 11:17:53 AM
Last modification on : Saturday, April 29, 2017 - 1:05:05 AM
Long-term archiving on: : Saturday, July 29, 2017 - 1:05:48 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Xiaoying Xu, Anindya Datta, Kaushik Dutta. Using Adjective Features from User Reviews to Generate Higher Quality and Explainable Recommendations. Working COnference on Shaping the Future of ICT Research, Dec 2012, Tampa, FL, United States. pp.18-34, ⟨10.1007/978-3-642-35142-6_2⟩. ⟨hal-01515862⟩



Record views


Files downloads