Opinionated Product Recommendation

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Title: Opinionated Product Recommendation
Authors: Dong, Ruihai
Schaal, Markus
O'Mahony, Michael P.
McCarthy, Kevin
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/8314
Date: 11-Jul-2013
Abstract: In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Keywords: Recommender systems;Experience web
DOI: 10.1007/978-3-642-39056-2_4
Language: en
Status of Item: Peer reviewed
Is part of: Delany, S.J. and Ontanon, S. (eds.). Proceedings 21st International Conference (ICCBR 2013) (Lecture Notes in Computer Science Volume 7969)
Conference Details: 21st International Conference (ICCBR 2013), Saratoga Springs, New York, USA, 8-11 July 2013
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection
Insight Research Collection

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