| 基于蚁群行为的动态挖掘用户导航模式兴趣模型 |
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| 引用本文： | 凌海峰,刘业政,杨善林. 基于蚁群行为的动态挖掘用户导航模式兴趣模型[J]. 计算机工程与应用, 2008, 44(17): 24-26. DOI: 10.3778/j.issn.1002-8331.2008.17.007 |
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| 作者姓名： | 凌海峰 刘业政 杨善林 |
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| 作者单位： | 1.合肥工业大学 管理学院，合肥 230009 2.过程优化与智能决策教育部重点实验室，合肥 230009 |
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| 摘 要： | 随着电子商务的快速发展，一个越来越重要的问题是如何挖掘并预测用户的导航模式。挖掘用户的导航模式是Web使用挖掘的一项重要任务，也是产生导航推荐的基本方法。由于用户的兴趣是不断变化的，因此很难准确跟踪用户的导航模式。在提出了一种蚁群模型来解决该问题。把Web用户看成是人工的蚂蚁，然后应用蚂蚁理论来指导用户在网站上的选择。首先，基于Web日志数据建立一个用户导航模型；其次，设计了一个算法，动态挖掘群体用户偏好的导航模式；最后，对真实数据集的实验结果表明该方法是有效的。
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| 关 键 词： | 电子商务 蚁群行为 Web使用挖掘 群体用户导航模式 |
| 收稿时间： | 2008-01-29 |
| 修稿时间： | 2008-3-10 |
User interest model based on ant colony behavior for dynamic mining of navigation patterns |
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| LING Hai-feng,LIU Ye-zheng,YANG Shan-lin. User interest model based on ant colony behavior for dynamic mining of navigation patterns[J]. Computer Engineering and Applications, 2008, 44(17): 24-26. DOI: 10.3778/j.issn.1002-8331.2008.17.007 |
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| Authors: | LING Hai-feng LIU Ye-zheng YANG Shan-lin |
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| Affiliation: | 1.School of Management，Hefei University of Technology，Hefei 230009，China 2.Key Laboratory of Process Optimization and Intelligent Decision-making，Ministry of Education，Hefei 230009，China |
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| Abstract: | With the rapid development of e-commerce，the importance of mining and predicting user’s navigation patterns grow larger than before.As an important task of Web usage mining，mining users’ navigation patterns is the fundamental approach for generating recommendations.But the users’ interests are changeable，and it is difficult to track the exact user navigation patterns.An ant colony approach for this problem is proposed in this paper.In this approach，the web users is been considered as artificial ants，and the ant colony behavior is used as a metaphor to guide user’s choice in the Web site.Firstly，a user navigation model is built，based on ant colony behavior and Web logs.Secondly，an algorithm for mining users preferred navigation patterns dynamically is designed.The experimental results proved the effectiveness of our approach. |
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| Keywords: | e-commerce ant colony behavior Web usage mining users navigation patterns |
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