一种预测群体用户访问行为的算法

在电子商务发展中,商家需要理解用户访问网站的行为,为用户提供个性化服务,从而吸引用户购买商品。挖掘用户访问网站的行为是商家一个急需解决的问题,通过对Web日志进行挖掘是解决该问题的重要研究方法。提出了网页兴趣信息素的新概念,它是由页面相对浏览时间和点击率构建而成,利用兴趣信息素设计了基于蚁群算法的群体用户访问路径挖掘算法,根据挖掘结果预测用户访问行为。实验结果表明,兴趣信息索可以有效地预测用户的兴趣变化,能准确地反映用户访问模式,提高了预测群体用户访问行为的准确率。     

挖掘Web日志降低信息搜寻的时间费用

如何根据用户的行为信息优化站点的设计是一个重要的研究问题．提出了一种新的支持站点设计优化的Web使用挖掘方案．此方案基于Web日志中的搜寻路径统计用户寻找目标花费的平均时间，用以量化Web页面的搜寻费用．在此基础上提出了一种高效的数据挖掘方法，寻找一组能够有效压缩搜寻路径(降低时间费用)的超链接．实验表明，挖掘的结果能够提供许多有用的信息，帮助管理者及时发现站点设计中存在的问题．     

Web数据挖掘技术

随着Intemet／Web技术的快速普及和迅猛发展,各种信息可以以非常低的成本在网络上获得。如何在这些信息中找到用户真正需要的内容,成为数据组织和Web相关领域专家学者关注的焦点。Web数据挖掘旨在发现隐藏在Web数据中潜在的有用知识、提供决策支持,已经成为数据挖掘领域中新兴的研究热点。该文主要从Web内容挖掘、Web结构挖掘和Web使用挖掘三个方面阐述Web数据挖掘的基本知识。     

ILP在语义Web数据挖掘研究中的应用

语义网络数据挖掘是基于语义网络环境的数据挖掘,它给数据挖掘技术的应用研究提出了新的课题。归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域,它为知识工程等人工智能的应用领域提供了新的强有力的技术支持。分析了现有几种常用数据挖掘技术在语义Web环境下应用的局限性,提出了采用归纳逻辑程序设计(ILP)作为语义Web上适合的数据挖掘技术,给出了应用这种技术的算法描述,通过具体实例验证了其可行性。     

Piggy Bank: Experience the Semantic Web inside your web browser

The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content.To break this chicken-and-egg problem, thus enabling more flexible information access, we have created a web browser extension called Piggy Bank that lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to re-structure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the existence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web's vision.To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build sophisticated Semantic Web information repositories through simple, everyday's use of Piggy Bank.     

Feature-based opinion mining through ontologies

The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.     

Web数据挖掘技术

随着Internet/Web技术的快速普及和迅猛发展,各种信息可以以非常低的成本在网络上获得,如何在这些信息中找到用户真正需要的内容,成为数据组织和Web相关领域专家学者关注的焦点。Web数据挖掘旨在发现隐藏在Web数据中潜在的有用知识、提供决策支持,已经成为数据挖掘领域中新兴的研究热点。该文主要从Web内容挖掘、Web结构挖掘和Web使用挖掘三个方面阐述Web数据挖掘的基本知识。     

基于WUM的个性化智能站点的研究

Internet和电子商务的发展带动了Web数据挖掘技术的发展.本文提出了针对提高Web服务质量的解决方案:采用WUM为核心技术,建立一个智能Web站点,通过建立与更新用户模式库,个性化地为用户的访问提供推荐服务.     

DYSCS: A platform to build geographically and semantically enhanced social content sites

Social content sites allow ordinary internet users to upload, edit, share, and annotate Web content with freely chosen keywords called tags. However, tags are only useful to the extent that they are processable by users and machines, which is often not the case since users frequently provide ambiguous and idiosyncratic tags. Thereby, many social content sites are starting to allow users to enrich their tags with semantic metadata, such as the GeoSocial Content Sites, for example, where users can annotate their tags with geographic metadata. But geographic metadata alone only unveils a very specific facet of a tag, which leads to the need for more general purpose semantic metadata. This paper introduces DYSCS – Do it Yourself Social Content Sites – a platform that combines Web 2.0 and Semantic Web technologies for assisting users in creating their own social content sites enriched with geographic and general purpose semantics. Moreover, DYSCS is highly reusable and interoperable, which are consequences of its ontology driven architecture.     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

基于Web使用挖掘的网站优化服务研究

Web使用挖掘是通过分析上网过程所产生的数据,发现网络用户访问行为的隐含模式,以此优化网站的设计,吸引潜在的客户。本文就Web使用挖掘技术在网站优化服务中的应用做了探讨和研究。     

一种基于后缀树的Web访问模式挖掘算法

在Web使用挖掘中，分析用户的行为模式是一个关键的问题。文中提出了一种基于后缀树的最大频繁序列MFS(Maximal Frequent Sequences)的有效挖掘算法，该算法能够从增量数据中动态发现和输出MFS。     

基于web挖掘的用户服务研究

数据丰富而知识贫乏导致了知识发现和数据挖掘领域的出现。基于Web的数据挖掘，是从Web海量的数据中自动、智能地抽取隐藏于这些数据中的知识，分析了Web挖掘技术的概念、特点、技术等。根据Web数据挖掘最流行的分类，可以分为Web内容挖掘、Web结构挖掘和Web使用记录挖掘。其中Web使用挖掘就是运用数据挖掘的思想来对服务器日志进行分析处理。该文根据Web数据挖掘的最近研究状况，主要论述了一个更新的频繁路径集的挖掘浏览模式在Web用户个性化服务中的应用，同时，还对发现的知识讨论了其在在线服务中的应用并给出了相应算法。     

基于概念格的Web日志路径挖掘算法

路径挖掘适用于探索用户沿超连接寻找和浏览网页的规律，而Web日志的完美结构使挖掘更加容易和有效。由二元关系导出的概念格作为一种非常有用的形式化工具，体现了概念内涵和外延的统一，反映了对象和特征间的联系以及概念的泛化与例化关系，因此非常适于发现数据中潜在的信息。本文通过概念格模型，提出了一种Web日志的路径挖掘算法，并进行了相关的分析与展望。     

个性化推荐技术在电子商务中的实现

随着互联网的发展,Web挖掘技术已经成为数据挖掘技术的一个研究的热点。本文对Web挖掘的特点、方法进行了讨论,提出了结合网页的链接结构来补充数据的预处理,以更精确地识别用户会话。同时在挖掘浏览模式的时候,结合网页内容聚类和用户聚类,提高了推荐系统的性能。     

基于机器学习的浏览器挖矿检测模型研究

浏览器挖矿通过向网页内嵌入挖矿代码,使得用户访问该网站的同时,非法占用他人系统资源和网络资源开采货币,达到自己获益的挖矿攻击。通过对网页挖矿特征进行融合,选取八个特征用以恶意挖矿攻击检测,同时使用逻辑回归、支持向量机、决策树、随机森林四种算法进行模型训练,最终得到了平均识别率高达98.7%的检测模型。同时经实验得出随机森林算法模型在恶意挖矿检测中性能最高;有无Websocket连接、Web Worker的个数和Postmessage及onmessage事件总数这三个特征的组合对恶意挖矿检测具有高标识性。     

基于粗糙集的Web结构挖掘

Web站点是由许多Web页面构成的信息系统,随着网络的飞速发展,Web挖掘得到了越来越多的研究。如何从Web中找到与用户查询主题相关的权威页面,是Web结构挖掘的一个重要研究方向。粗糙集理论作为一种有效处理模糊和不确定信息的数学工具,由于其不需要任何先验知识,在数据挖掘领域取得了广泛的应用。文中概述了Web结构挖掘的有关概念,基于粗糙集理论,定义了Web结构挖掘的数据模型,并给出了基于粗糙集的Web结构挖掘的实现流程,分析说明了该方法的性能。     

基于Web日志挖掘的信息无障碍网站设计研究

针对当前一些实现信息无障碍网站存在的问题,该文设计了一个网站设计架构,利用Web日志挖掘技术提取用户兴趣和访问优先序列,采取网站页面信息动态填充至网站主页通用框架模块中,实现智能化、个性化无障碍访问。     

Web使用模式挖掘技术在网站营销中的应用

Web使用模式挖掘是Web数据挖掘的重要内容之一，其应用领域非常广泛。将Web数据挖掘技术应用于电子商务网站的营销中，可发现许多有用的信息，有效地使用这些信息可促进电子商务网站的发展。