Dealing with diversity and novelty in group recommendations using Hesitant fuzzy sets

Castro, J; Barranco, MJ; Rodríguez, RM; Martínez, L

Dealing with diversity and novelty in group recommendations using Hesitant fuzzy sets

Castro, J

Barranco, MJ Rodríguez, RM Martínez, L

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2017
Issue Date:: 2017-08-23

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Field	Value	Language
dc.contributor.author	Castro, J https://orcid.org/0000-0002-8390-8238	en_US
dc.contributor.author	Barranco, MJ	en_US
dc.contributor.author	Rodríguez, RM	en_US
dc.contributor.author	Martínez, L	en_US
dc.date.issued	2017-08-23	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2017	en_US
dc.identifier.isbn	9781509060344	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125957
dc.description.abstract	© 2017 IEEE. Diversity and novelty are appreciated features by users of recommender systems, which alleviate the information overload problem. These features are more important in recommendation to groups because members interests and needs differ from each other or are even in conflict. Various techniques have been used to recommend to groups. However, these techniques apply an aggregation step that imply a loss of information, which negatively affect the recommendation. We aim at avoiding the negative influence of the aggregation step considering the various interests and needs of the group members as the group hesitation, thus, our proposal uses Hesitant Fuzzy Sets to model the group information. A case study is performed to evaluate the proposal, whose results show its performance regarding recommendation diversity, novelty and accuracy.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2017.8015484	en_US
dc.title	Dealing with diversity and novelty in group recommendations using Hesitant fuzzy sets	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Diversity and novelty are appreciated features by users of recommender systems, which alleviate the information overload problem. These features are more important in recommendation to groups because members interests and needs differ from each other or are even in conflict. Various techniques have been used to recommend to groups. However, these techniques apply an aggregation step that imply a loss of information, which negatively affect the recommendation. We aim at avoiding the negative influence of the aggregation step considering the various interests and needs of the group members as the group hesitation, thus, our proposal uses Hesitant Fuzzy Sets to model the group information. A case study is performed to evaluate the proposal, whose results show its performance regarding recommendation diversity, novelty and accuracy.

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http://hdl.handle.net/10453/125957