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

SUMMARY Clickstreams in users’ navigation logs have various datawhich are related to users’ web surfing. Those are visit counts, stay times,product types, etc. When we observe these data, we can divide clickstreamsinto sub-clickstreams so that the pages in a sub-clickstream share more con-texts with each other than with the pages in other sub-clickstreams. In thispaper, we propose a method which extracts more informative rules fromclickstreams for web page recommendation based on genetic programmingand association rules. First, we split clickstreams into sub-clickstreamsby contexts for generating more informative rules. In order to split click-streams in consideration of context, we extract six features from users’ nav-igation logs. A set of split rules is generated by combining those featuresthrough genetic programming, and then informative rules for recommen-dation are extracted with the association rule mining algorithm. Throughexperiments, we verify that the proposed method is more effective than theother methods in various conditions.

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