This paper proposes an intelligent service method on personalized recommendation.which is based on association rules mining for Internet. To alleviate the phenomena of 'information overload' and 'information bewilderment 'in Internet environment, the overall process can be divided into two components: offline part and online part- In offline, Web mining tasks can execute in the logs of Web service resulting in a user transaction file, and the frequent user transaction patterns are extracted by filtering with thresholds of support again, afterwards, constructing aggregating tree of user sessions. In online, the candidate URLs for recommendation can be determined by matching association rules in the aggregating tree with the current active session for the intelligent services of personalization recommendation- The experiments demonstrate that our approach is applicable and effective.
This paper proposes an intelligent service method on personalized recommendation.which is based on association rules mining for Internet. To alleviate the phenomena of 'information overload' and 'information bewilderment 'in Internet environment, the overall process can be divided into two components: offline part and online part- In offline, Web mining tasks can execute in the logs of Web service resulting in a user transaction file, and the frequent user transaction patterns are extracted by filtering with thresholds of support again, afterwards, constructing aggregating tree of user sessions. In online, the candidate URLs for recommendation can be determined by matching association rules in the aggregating tree with the current active session for the intelligent services of personalization recommendation- The experiments demonstrate that our approach is applicable and effective.
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