Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-35142-6-2
Title: Using adjective features from user reviews to generate higher quality and explainable recommendations
Authors: Xu, X.
Datta, A. 
Dutta, K.
Keywords: Adjective Features
Recommender systems
Sparsity
Transparency
User reviews
Issue Date: 2012
Source: Xu, X.,Datta, A.,Dutta, K. (2012). Using adjective features from user reviews to generate higher quality and explainable recommendations. IFIP Advances in Information and Communication Technology 389 AICT : 18-34. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-35142-6-2
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. © 2012 IFIP International Federation for Information Processing.
Source Title: IFIP Advances in Information and Communication Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/42861
ISBN: 9783642351419
ISSN: 18684238
DOI: 10.1007/978-3-642-35142-6-2
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Jan 9, 2018

Page view(s)

80
checked on Jan 11, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.