DocumentCode :
2626478
Title :
Semantic-Enhanced Case-Based Reasoning for Intelligent Recommendation
Author :
Wang, Huimin ; Nie, Guihua ; Chen, Donglin
Author_Institution :
Sch. of Econ., Wuhan Univ. of Technol., Wuhan, China
Volume :
5
fYear :
2009
fDate :
March 31 2009-April 2 2009
Firstpage :
697
Lastpage :
701
Abstract :
Case-based reasoning techniques have been applied to recommender systems. In this paper, we have presented a new intelligent recommendation approach that combines semantic Web techniques with case- based recommendation techniques to improve the performance of recommender systems. The proposed case model integrates both content information and rating information. Instead of using syntactic techniques, case similarity between the current case and a retrieved case is measured based on semantic similarity algorithm in order to understand and reuse cases well stored in distributed case bases. The domain ontologies provide a formal representation which includes semantic descriptions of users and products. The proposed approach that considers semantic information of both the products´ content descriptions and the user´s preferences overcomes the limitations of the traditional recommender systems.
Keywords :
case-based reasoning; groupware; information filters; information retrieval; ontologies (artificial intelligence); semantic Web; case-based recommendation technique; collaboration-based case modeling approach; formal representation; intelligent recommendation approach; ontology; product-based case modeling approach; recommender system; semantic Web technique; semantic-enhanced case-based reasoning; Collaboration; Computer science; Current measurement; Filtering; History; Hybrid power systems; Intelligent systems; Ontologies; Recommender systems; Semantic Web; Case-based reasoning; intelligent recommendation; semantic web;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
Type :
conf
DOI :
10.1109/CSIE.2009.634
Filename :
5170623
Link To Document :
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