A classification-based review recommender

Many online stores encourage their users to submit product or service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the most-helpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.

[1]  Raymond J. Mooney,et al.  Explaining Recommendations: Satisfaction vs. Promotion , 2005 .

[2]  Andrea Esuli,et al.  Multi-Faceted Rating of Product Reviews , 2009, ERCIM News.

[3]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

[4]  James Caverlee,et al.  Ranking Comments on the Social Web , 2009, 2009 International Conference on Computational Science and Engineering.

[5]  Xiaohui Yu,et al.  Modeling and Predicting the Helpfulness of Online Reviews , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[6]  F. Maxwell Harper,et al.  Facts or friends?: distinguishing informational and conversational questions in social Q&A sites , 2009, CHI.

[7]  Padraig Cunningham,et al.  An Assessment of Machine Learning Techniques for Review Recommendation , 2009, AICS.

[8]  M. de Rijke,et al.  Credibility Improves Topical Blog Post Retrieval , 2008, ACL.

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10]  Barry Smyth,et al.  Learning to recommend helpful hotel reviews , 2009, RecSys '09.

[11]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[12]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[13]  Ian Witten,et al.  Data Mining , 2000 .

[14]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[15]  Nava Tintarev.  The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data , 2008, AH.