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| 基于Web-Log Mining的N元预测模型 | |
| 引用本文： | 苏中,马少平,杨强,张宏江.基于Web-Log Mining的N元预测模型[J].软件学报,2002,13(1):136-141. |
| 作者姓名： | 苏中 马少平 杨强 张宏江 |
| 作者单位： | 1. 清华大学,计算机科学与技术系,北京,100084;清华大学,智能技术与系统国家重点实验室,北京,100084 2. Simon,Fraser大学,加拿大 3. 微软中国研究院,北京,100080 |
| 基金项目： | 国家重点基础研究发展规划973资助项目(G1998030509) |
| 摘 要： | 随着Web上用户访问信息的不断增加,特别是Web服务器可提供大量的日志文件,使得有可能对这些大数据集进行知识挖掘,例如,对用户未来的访问进行预测.提出了一种利用服务器日志文件,运用N元(N-gram)预测模型对用户未来可能进行的Web访问请求进行预测.这种模型会选择性地对用户可预测的请求进行预测,从而大大提高了预测精度.实验证明,在自然语言中普遍适用的N元预测模型同样适用于网页预测.同时,采用了一种有效的简化手段,大大压缩了模型的大小,使得5元模型和传统的2元模型大小基本相同,而预测精度提高了1倍.该结果可以广泛地运用到Web上,包括网页的预发送、预取、推荐以及Web上的caching机制.试验是建立在真实的Web日志上的,该算法无论在预测精度上还是在可适用度上都优于以往的算法. |
| 关 键 词： | Web mining 数据挖掘 预测 |
| 文章编号： | 1000-9825/2002/13(01)0136-06 |
| 收稿时间： | 4/3/2000 12:00:00 AM |
| 修稿时间： | 2000年4月3日 |
An N-Gram Prediction Model Based on Web-Log Mining |
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| SU Zhong,MA Shao-ping,YANG Qiang and ZHANG Hong-jiang.An N-Gram Prediction Model Based on Web-Log Mining[J].Journal of Software,2002,13(1):136-141. | |
| Authors: | SU Zhong MA Shao-ping YANG Qiang and ZHANG Hong-jiang |
| Abstract: | As an increasing number of users access information on the Web, there is a great opportunity to learn about the users?probable actions in the future from the server logs. In this paper, an n-gram based model is presented to utilize path profiles of users from very large data sets to predict the users?future requests. Since this is a prediction system, the recall cannot be measured in a traditional sense. Therefore, the notion of applicability is presented to give a measure of the ability to predict the next document.The new model is based on a simple extension of existing point-based models for such predictions,but the results show that by sacrificing the applicability somewhat one can gin a great deal in prediction precision.The result can potentially be applied to awide range of applications on the Web,including pre-sending,pre-fetching,enhancement of recommendation systems as well as Web caching po;icies.The tests are based on three realistic Web logs.The new algorithm shows a marked improvement in precision and applicability over previous approaches. |
| Keywords: | Web mining data mining prediction |
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