Sentimental Product Recommendation

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Title: Sentimental Product Recommendation
Authors: Dong, Ruihai
O'Mahony, Michael P.
Schaal, Markus
McCarthy, Kevin
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/8456
Date: 16-Oct-2013
Abstract: This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2013 ACM
Keywords: Recommender systems;User-generated reviews;Opinion mining;Sentiment-based product recommendation
DOI: 10.1145/2507157.2507199
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of RecSys '13: 7th ACM conference on Recommender systems
Conference Details: RecSys '13: 7th ACM conference on Recommender systems, Hong Kong, China, 12-16 October 2013
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection
Insight Research Collection

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