A hybrid Multi-Criteria Semantic-enhanced collaborative filtering approach for personalized recommendations

Shambour, Q; Lu, J

A hybrid Multi-Criteria Semantic-enhanced collaborative filtering approach for personalized recommendations

Shambour, Q Lu, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011, 2011, 1 pp. 71 - 78
Issue Date:: 2011-11-07

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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	2011-11-07	en_US
dc.identifier.citation	Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011, 2011, 1 pp. 71 - 78	en_US
dc.identifier.isbn	9780769545134	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119949
dc.description.abstract	Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and coldstart problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011	en_US
dc.relation.isbasedon	10.1109/WI-IAT.2011.109	en_US
dc.title	A hybrid Multi-Criteria Semantic-enhanced collaborative filtering approach for personalized recommendations	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
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
pubs.volume	1	en_US

Abstract:

Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and coldstart problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques. © 2011 IEEE.

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http://hdl.handle.net/10453/119949