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User characteristics analysis based on web log mining

机译:基于Web日志挖掘的用户特征分析

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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 token to the push service repository on the basis of the experimental results, which achieved good practical results.
机译:为解决大规模网络信息与有限的学习需求之间的矛盾,个性化推荐服务成为研究区域的热点。用户特性分析是个性化推荐服务中的关键点。基于Web服务器中的大量Web访问日志,本研究逐渐提出了用户特征分析的步骤,包括数据预处理,用户特征提取和用户群集。本文重点介绍了用户识别规则,用户功能的定义,用户特征提取算法和用户组聚类。最后,将Web服务器的访问日志文件作为样本,已经进行了模拟实验,以证明通过这种背景提出的思想。在实验结果的基础上,改进已经令牌到推送服务储存库,这取得了良好的实际结果。

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