Mining User Interests in Web Logs of an Online News Service Based on Memory Model

2013 IEEE Eighth International Conference on Networking, Architecture and Storage Pub Date : 2013-07-01 DOI:10.1109/NAS.2013.25

Wei Wang, Dongyan Zhao, Haining Luo, Xin Wang

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引用次数: 9

Abstract

User profiling plays an important role in online news recommendation systems. In this paper, we analyze the relationship between users' clicking behaviors and the category of the news story to model user's interests by mining web log data of an adaptive news system. We train a Memory-based User Profile (MUP), which imitates human being's learning, remembering and forgetting mechanisms, to predict users' potential interests dynamically. We mainly focus on experimental analysis to refine the MUP scheme. Firstly, we materialize the meanings of all parameters of MUP by important factors (i.e., absorbing factor, forgetting factor, timescale and learning strength) in human being's learning and forgetting process. Secondly, we demonstrate how to determine the values of parameters for different users to reflect their distinct learning and forgetting abilities. Thirdly, we derive a threshold from MUP's recursion formula, which can be used to simply distinguish long-term and short-term interests. Our evaluations are carried out on IdoIcan's web log data, results show MUP can model user's profile effectively.

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基于记忆模型的在线新闻服务日志用户兴趣挖掘

用户特征分析在在线新闻推荐系统中起着重要的作用。本文通过挖掘自适应新闻系统的网络日志数据，分析用户点击行为与新闻类别之间的关系，建立用户兴趣模型。我们训练了一个基于记忆的用户模型(MUP)，该模型模仿人类的学习、记忆和遗忘机制，动态预测用户的潜在兴趣。我们主要通过实验分析来完善MUP方案。首先，我们通过人类学习和遗忘过程中的重要因素(即吸收因素、遗忘因素、时间尺度和学习强度)来物化MUP各参数的含义。其次，我们演示了如何确定不同用户的参数值，以反映他们不同的学习和遗忘能力。第三，我们从MUP的递归公式中推导出一个阈值，可以简单地区分长期和短期利益。我们对IdoIcan的网络日志数据进行了评估，结果表明MUP可以有效地模拟用户的个人资料。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 IEEE Eighth International Conference on Networking, Architecture and Storage

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