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Recommending from Experience

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
In this paper we present RC, a context-driven recommender system that mines contextual information from user-generated reviews and makes recommendations based on the users’ experiences. RC mines the contextual information from the user-generated reviews using a form of topic modeling. This means that, unlike other context-aware recommender systems, RC does not have a predefined set of contextual variables. After mining the contextual information, RC makes top-n recommendations using a Factorization Machine with the contextual topics as side information. Our experiments on two datasets of ratings and reviews show that RC has higher recall than a conventional recommender.
Conference Name: 
The Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS-30)
Proceedings: 
https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/schedConf/presentations
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
22/05/2017
Pages: 
651-656
Conference Location: 
United States of America
Research Group: 
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
Yes
Publication document: