Mining Experiential Product Cases

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Title: Mining Experiential Product Cases
Authors: Dong, Ruihai
Schaal, Markus
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/7395
Date: 11-Jul-2013
Abstract: Case-based reasoning (CBR) attempts to reuse past experiences to solve new problems. CBR ideas are commonplace in recommendation systems, which rely on the similarity between product queries and a case base of product cases. But, the relationship between CBR and many of these recommenders can be tenuous: the idea that product cases made up of static meta-data type features are experiential is a stretch; unless one views the type of case descriptions used by collaborative filtering (user ratings across products) as experiential. Here we explore and evaluate how to automatically generate product cases from user-generated reviews to produce cases that are based on genuine user experiences for use in a case-based product recommendation system.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Keywords: Recommender systemsNatural language processingText miningSentiment analsysis
Other versions: http://www.iccbr.org/iccbr13/
Language: en
Status of Item: Peer reviewed
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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