Mining Experiential Product Cases
|Title:||Mining Experiential Product Cases||Authors:||Dong, Ruihai
O'Mahony, Michael P.
|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 systems; Natural language processing; Text mining; Sentiment 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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