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An assessment of machine learning techniques for review recommendation
Date Issued
2009-08
Date Available
2009-11-27T14:47:12Z
Abstract
In this paper, we consider a classification-based approach to the recommendation of user-generated product reviews. In particular, we develop review ranking techniques that allow the most helpful reviews for a particular product to be recommended, thereby facilitating users to readily asses the quality of the product in question. We apply a
supervised machine learning approach to this task and compare the performance achieved by several classification algorithms using a large-scale study based on TripAdvisor hotel reviews. Our findings indicate that our approach is successful in recommending helpful reviews compared
to benchmark ranking schemes, and further we highlight an interesting performance asymmetry that is biased in favour of reviews expressing negative sentiment.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Copyright (Published Version)
Springer-Verlag Berlin Heidelberg 2010
Subject – LCSH
Machine learning
Recommender systems (Information filtering)
User-generated content--Classification
Web versions
Language
English
Status of Item
Not peer reviewed
Part of
L. Coyle, J. Freyne (ed.s). Artificial Intelligence and Cognitive Science : 20th Irish Conference, AICS 2009 Dublin, Ireland, August 19-21, 2009 : Revised Selected Papers, LNAI 6206
Conference Details
Presentation at the 20th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 09), Dublin, 19th - 21st August 2009
ISBN
978-3-642-17079-9
This item is made available under a Creative Commons License
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