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A classification-based review recommender
Author(s)
Date Issued
2009-12
Date Available
2010-01-22T15:22:36Z
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.
Sponsorship
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
Web versions
Language
English
Status of Item
Peer reviewed
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
ISBN
978-1-84882-982-4
This item is made available under a Creative Commons License
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