Sentimental Product Recommendation
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|Title:||Sentimental Product Recommendation||Authors:||Dong, Ruihai
O'Mahony, Michael P.
|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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