Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems

Shambour, Q; Lu, J

Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems

Shambour, Q Lu, J

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Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, 2011, pp. 44 - 51
Issue Date:: 2011-08-10

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Field	Value	Language
dc.contributor.author	Shambour, Q	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2011-08-10	en_US
dc.identifier.citation	IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, 2011, pp. 44 - 51	en_US
dc.identifier.isbn	9781612840697	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119948
dc.description.abstract	Recommender Systems are information systems that attempt to recommend items of interest to particular users based on their explicit and implicit preferences. Multi-Criteria Decision Making (MCDM) aims at assisting the decision maker in the decision making process, or giving the decision maker a recommendation, concerning a set of actions, alternatives, items etc. Thus, despite their differences, Recommender Systems and Multi-Criteria Decision Making share the same objective which is supporting the decision making process and reducing information overload. In this paper we propose a novel hybrid Multi-Criteria Trust-enhanced CF (MC-TeCF) approach. The proposed MC-TeCF approach combines the MC user-based CF and the MC user-based Trust filtering approaches to alleviate the standard Single-Criteria user-based CF limitations. Empirical results demonstrate the significance and effectiveness of the proposed MC-TeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold start users compared with the standard Single-Criteria user-based CF approach. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making	en_US
dc.relation.isbasedon	10.1109/SMDCM.2011.5949274	en_US
dc.title	Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recommender Systems are information systems that attempt to recommend items of interest to particular users based on their explicit and implicit preferences. Multi-Criteria Decision Making (MCDM) aims at assisting the decision maker in the decision making process, or giving the decision maker a recommendation, concerning a set of actions, alternatives, items etc. Thus, despite their differences, Recommender Systems and Multi-Criteria Decision Making share the same objective which is supporting the decision making process and reducing information overload. In this paper we propose a novel hybrid Multi-Criteria Trust-enhanced CF (MC-TeCF) approach. The proposed MC-TeCF approach combines the MC user-based CF and the MC user-based Trust filtering approaches to alleviate the standard Single-Criteria user-based CF limitations. Empirical results demonstrate the significance and effectiveness of the proposed MC-TeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold start users compared with the standard Single-Criteria user-based CF approach. © 2011 IEEE.

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http://hdl.handle.net/10453/119948