Using Association Rules to Solve the Cold-Start Problem in Recommender Systems
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 it is difficult for a recommender system to make quality recommendations. This problem is known as the cold-start problem. Here we investigate using association rules as a source of information to expand a user profile and thus avoid this problem. Our experiments show that it is possible to use association rules to noticeably improve the performance of a recommender system under the cold-start situation. Furthermore, we also show that the improvement in performance obtained can be achieved while using non-redundant rule sets. This shows that non-redundant rules do not cause a loss of information and are just as informative as a set of association rules that contain redundancy.
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|Item Type:||Journal Article|
|Keywords:||Multi-Level Association Rules, Non-redundant Association Rules, Recommender System, Cold-Start|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems not elsewhere classified (080699)
|Divisions:||Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2010 Springer.|
|Deposited On:||17 Feb 2011 00:08|
|Last Modified:||18 Jul 2014 04:51|
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