Investigating the use of association rules in improving recommender systems
Data(s) |
04/12/2009
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Resumo |
Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus when there is not sufficient knowledge on a user it is difficult for a recommender system to make quality recommendations. This problem is often referred to as the cold-start problem. Here we investigate whether association rules can be used as a source of information to expand a user profile and thus avoid this problem, leading to improved recommendations to users. Our pilot study shows that indeed it is possible to use association rules to improve the performance of a recommender system. This we believe can lead to further work in utilising appropriate association rules to lessen the impact of the cold-start problem. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/29797/2/29797.pdf http://es.csiro.au/adcs2009/proceedings/ Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2009) Investigating the use of association rules in improving recommender systems. In ADCS 2009 : HCSNet Summerfest 09, 14th Australasian Document Computing Symposium , 30 Nov. - 4 Dec., 2009, University of New South Wales, Sydney, Australia. |
Direitos |
Copyright 2009 please contact the authors |
Fonte |
Faculty of Science and Technology; School of Information Technology |
Palavras-Chave | #080699 Information Systems not elsewhere classified #080704 Information Retrieval and Web Search #Information Retrieval #Personalised Documents #Recommender Systems #Association Rules |
Tipo |
Conference Item |