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2002

Efficient Adaptive-Support Association Rule Mining for Recommender Systems

8 years 6 months ago
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in ad...
Weiyang Lin, Sergio A. Alvarez, Carolina Ruiz
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2002
Where DATAMINE
Authors Weiyang Lin, Sergio A. Alvarez, Carolina Ruiz
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