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/10197/2000
Date: May-2010
Abstract: 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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Knowledge-Based Systems
Volume: 23
Issue: 4
Start page: 323
End page: 329
Copyright (published version): 2010 Elsevier
Keywords: User-generated reviewsClassificationHelpfulTripAdvisor
Subject LCSH: User-generated content--Classification
Recommender systems (Information filtering)
Automatic classification;
DOI: 10.1016/j.knosys.2009.11.004
Other versions: http://dx.doi.org/10.1016/j.knosys.2009.11.004
Language: en
Status of Item: Peer reviewed
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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