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Using Association Rules to Solve the Cold-Start Problem in Recommender Systems

Authors :
Zaki, M J
Pudi, V
Xu Yu, J
Ravindran, B
Shaw, Gavin
Xu, Yue
Geva, Shlomo
Zaki, M J
Pudi, V
Xu Yu, J
Ravindran, B
Shaw, Gavin
Xu, Yue
Geva, Shlomo
Source :
Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010 Proceedings [Lecture Notes in Computer Science, Volume 6118]
Publication Year :
2010

Abstract

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.

Details

Database :
OAIster
Journal :
Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010 Proceedings [Lecture Notes in Computer Science, Volume 6118]
Notes :
application/pdf
Publication Type :
Electronic Resource
Accession number :
edsoai.on1146602055
Document Type :
Electronic Resource