Cui Zhang1,*, Xiaofei Li2
1School of Electrical and Automation Engineering, Liaoning Institute of Science and Technology, Benxi, Liaoning, China.
2MCC Coke Resistance (Dalian) Engineering Technology Co., Ltd, Dalian, Liaoning, China.
*Corresponding author: Cui Zhang
Abstract
In order to solve the contradiction between massive network information and limited learning needs, personalized recommendation service becomes the hotspot in research area. User characteristics analysis is the key point in personalized recommendation service. Based on the massive web access log in the web server, this research gradually puts forward the steps of user characteristics analysis which including data pretreatment, user feature extraction and user clustering. This paper focuses on the rules of user recognition, definition of user feature, user feature extraction algorithm and user group clustering. Finally, take access log files of a web server as sample, simulation experiments have been made to prove the thought put forward by this context. Improvement has been taken to the push service repository on the basis of the experimental results, which achieved good practical results. Behavior characteristics and laws of visitors access based on log analysis may lay the foundation of recommender service for resources platform.
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