A fuzzy relational approach to event recommendation

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dc.contributor.author Cornelis, C
dc.contributor.author Guo, X
dc.contributor.author Lu, J
dc.contributor.author Zhang, G
dc.date.accessioned 2010-05-18T06:46:11Z
dc.date.issued 2005
dc.identifier.citation Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005, 2005, pp. 2231 - 2242
dc.identifier.isbn 0972741216
dc.identifier.isbn 9780972741217
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/6632
dc.description.abstract Most existing recommender systems employ collaborative filtering (CF) techniques in making projections about which items an e- service user is likely to be interested in, i.e. they identify correlations between users and recommend items which similar users have liked in the past. Traditional CF techniques, however, have difficulties when confronted with sparse rating data, and cannot cope at all with time-specific items, like events, which typically receive their ratings only after they have finished. Content-based (CB) algorithms, which consider the internal structure of items and recommend items similar to those a user liked in the past can partly make up for that drawback, but the collaborative feature is totally lost on them. In this paper, modelling user and item similarities as fuzzy relations, which allow to flexibly reflect the graded/uncertain information in the domain, we develop a novel, hybrid CF-CB approach whose rationale is concisely summed up as "recommending future items if they are similar to past ones that similar users have liked", and which surpasses related work in the same spirit. Copyright © IICAI 2005.
dc.title A fuzzy relational approach to event recommendation
dc.type Conference Proceeding
dc.parent Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005
dc.journal.number en_US
dc.publocation Tallahassee, USA en_US
dc.publocation London and New York
dc.publocation Tallahassee, USA
dc.publocation Tallahassee, USA
dc.identifier.startpage 2231 en_US
dc.identifier.endpage 2243 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference en_US
dc.conference Verified OK en_US
dc.conference Indian International Conference on Artificial Intelligence
dc.conference Indian International Conference on Artificial Intelligence
dc.conference.location Pune, INDIA en_US
dc.for 010206 Operations Research
dc.for 080605 Decision Support and Group Support Systems
dc.for 010303 Optimisation
dc.personcode 001038
dc.personcode 020014
dc.percentage 60 en_US
dc.classification.name Decision Support and Group Support Systems en_US
dc.classification.type FOR-08 en_US
dc.edition 1
dc.custom Indian International Conference on Artificial Intelligence en_US
dc.date.activity 20051220 en_US
dc.date.activity 2005-12-20
dc.date.activity 2005-12-20
dc.location.activity Pune, INDIA en_US
dc.location.activity Pune, INDIA
dc.location.activity Pune, INDIA
pubs.embargo.period Not known
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 - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true


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