An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming

Jaekwang KIM  KwangHo YOON  Jee-Hyong LEE  

IEICE TRANSACTIONS on Communications   Vol.E95-B   No.5   pp.1558-1565
Publication Date: 2012/05/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E95.B.1558
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Frontiers of Information Network Science)
association rule mining,  web page recommendation,  genetic programming,  clickstream,  user navigational log,  context,  

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Clickstreams in users' navigation logs have various data which are related to users' web surfing. Those are visit counts, stay times, product types, etc. When we observe these data, we can divide clickstreams into sub-clickstreams so that the pages in a sub-clickstream share more contexts with each other than with the pages in other sub-clickstreams. In this paper, we propose a method which extracts more informative rules from clickstreams for web page recommendation based on genetic programming and association rules. First, we split clickstreams into sub-clickstreams by contexts for generating more informative rules. In order to split clickstreams in consideration of context, we extract six features from users' navigation logs. A set of split rules is generated by combining those features through genetic programming, and then informative rules for recommendation are extracted with the association rule mining algorithm. Through experiments, we verify that the proposed method is more effective than the other methods in various conditions.