QUT ePrints

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

Shaw, Gavin, Xu, Yue, & Geva, Shlomo (2010) Using Association Rules to Solve the Cold-Start Problem in Recommender Systems. Lecture Notes in Computer Science, 6118, pp. 340-347.

View at publisher

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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® 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 the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

269 since deposited on 17 Feb 2011
103 in the past twelve months

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.

ID Code: 40176
Item Type: Journal Article
Keywords: Multi-Level Association Rules, Non-redundant Association Rules, Recommender System, Cold-Start
DOI: 10.1007/978-3-642-13657-3_37
ISBN: 978-3-642-13656-6
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 10:08
Last Modified: 01 Mar 2012 00:30

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page