A classification-based review recommender
|Title:||A classification-based review recommender||Authors:||O'Mahony, Michael P.
|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 reviews; Classification; Helpful; TripAdvisor||Subject LCSH:||User-generated content--Classification
Recommender systems (Information filtering)
|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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