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    吴 晶 张 品 罗 辛 盛 浩 熊 璋. 门户个性化兴趣获取与迁移模式发现[J]. 计算机研究与发展, 2007, 44(8): 1284-1292.
    引用本文: 吴 晶 张 品 罗 辛 盛 浩 熊 璋. 门户个性化兴趣获取与迁移模式发现[J]. 计算机研究与发展, 2007, 44(8): 1284-1292.
    Wu Jing, Zhang Pin, Luo Xin, Sheng Hao, and Xiong Zhang. Mining Interests and Navigation Patterns in Personalization on Portal[J]. Journal of Computer Research and Development, 2007, 44(8): 1284-1292.
    Citation: Wu Jing, Zhang Pin, Luo Xin, Sheng Hao, and Xiong Zhang. Mining Interests and Navigation Patterns in Personalization on Portal[J]. Journal of Computer Research and Development, 2007, 44(8): 1284-1292.

    门户个性化兴趣获取与迁移模式发现

    Mining Interests and Navigation Patterns in Personalization on Portal

    • 摘要: 个性化服务技术为门户平台上的兴趣挖掘研究带来了新的挑战,如何隐式地获取门户用户兴趣行为以及发现兴趣迁移模式是其中的重要课题.在对门户个性化兴趣映射描述的基础上,提出了一种独立于门户平台的含隐私保护的门户个性化兴趣获取机制,可实现不同兴趣访问行为的隐式获取以及操作语义分析,并采用兴趣扩展规则描述方式进行了隐私保护.结合门户个性化兴趣影响以及兴趣目的预测,给出了带有门户个性化兴趣描述的隐Markov模型扩展,可用于发现不同用户的门户个性化兴趣迁移模式.最后通过验证实验给出了有效性和可行性的结论分析.

       

      Abstract: Personalization services pose new challenges to interest mining on portal, such as many powerful functions to customize desktops. Capturing these surfing behaviors of users implicitly and mining interest navigation patterns are the top demanding tasks. Based on the summary of personalized interest mapping on portal, a novel portal-indepeodent mechanism of interest elicitation with privacy preserving is proposed, which implements both the implicit extraction of diverse access behaviors and corresponding semantic analysis. A new schema is also presented to extend the interest representation rule efficiently in privacy preserving process. Then the legal transparent accountable interest in terms of the interest behavior entries could be implied with this interest-extended rule. Moreover, the navigation relationship among the interests can describe the user's next possible interest trends, especially benefiting the recommendation. By combining the association effect of those interests and the prediction on interest intentions, a hidden Markov model is extended with personalized interest descriptions of portal to form interest navigation patterns for different users. Then experiments are carried out in order to validate the proposed approaches with availability and feasibility. The improvement of representation accuracy and mining capability for the complex interests on portal is a feature that clearly distinguishes the proposed approaches from traditional ones.

       

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