An assessment of machine learning techniques for review recommendation
|Title:||An assessment of machine learning techniques for review recommendation||Authors:||O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/1656||Date:||Aug-2009||Online since:||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.||Funding Details:||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)
|DOI:||10.1007/978-3-642-17080-5_26||Other versions:||http://dx.doi.org/10.1007/978-3-642-17080-5_26||Language:||en||Status of Item:||Not peer reviewed||Is part of:||L. Coyle, J. Freyne (ed.s).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|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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