Dong, RuihaiRuihaiDongO'Mahony, Michael P.Michael P.O'MahonySchaal, MarkusMarkusSchaalMcCarthy, KevinKevinMcCarthySmyth, BarryBarrySmyth2017-04-282017-04-282013 ACM2013-10-16http://hdl.handle.net/10197/8456RecSys '13: 7th ACM conference on Recommender systems, Hong Kong, China, 12-16 October 2013This 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.en© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of RecSys '13: 7th ACM conference on Recommender systems (2013) http://doi.acm.org/10.1145/2507157.2507199.Recommender systemsUser-generated reviewsOpinion miningSentiment-based product recommendationConference Publication41141410.1145/2507157.25071992015-06-15https://creativecommons.org/licenses/by-nc-nd/3.0/ie/