Investigating the use of association rules in improving recommender systems
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.
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
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Item (Poster)|
|Keywords:||Information Retrieval, Personalised Documents, Recommender Systems, Association Rules|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems not elsewhere classified (080699)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > School of Information Technology
|Copyright Owner:||Copyright 2009 please contact the authors|
|Deposited On:||20 Jan 2010 09:56|
|Last Modified:||01 Mar 2012 00:12|
Repository Staff Only: item control page