A classification-based review recommender

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Title: A classification-based review recommender
Authors: O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/1803
Date: Dec-2009
Abstract: Many online stores encourage their users to submit product/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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2010, Springer-Verlag London Limited
Subject LCSH: Recommender systems (Information filtering)
Automatic classification
User-generated content--Classification
DOI: 10.1007/978-1-84882-983-1_4
Language: en
Status of Item: Peer reviewed
Is part of: Bramer, M., Ellis, R., Petridis, M. (eds.). Research and Development in Intelligent Systems XXVI : Incorporating Applications and Innovations in Intelligent Systems XVII
Conference Details: Paper presented at Twenty-ninth SGAI International Conference (AI-2009), Cambridge, UK, 15th-17th December 2009
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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