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首页> 外文OA文献 >Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs.
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Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs.

机译：通过新颖的挖掘技术将用户数据转化为用户价值，以提取Web内容，结构和使用模式。新的Web挖掘方法的开发和评估，该方法可增强信息检索和增进对网站和社交博客中用户Web行为的理解。

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摘要

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer.udWith these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs.
机译：过去十年来，万维网的快速发展使其成为世界上最大的可公开访问的数据源，它已成为现代最重要，最有影响力的信息革命之一。 Web的影响几乎影响了人类生活，活动和领域的各个方面，引起了范式转换以及业务，治理和教育方面的变革。此外，在过去的几年中，Web 2.0和社交网站（例如社交博客和友谊网站）的快速发展，已将Web彻底从原始的信息消费环境转变为动态的，丰富的信息生产和共享平台。全世界。但是，Web的这种增长和转换导致了文本内容的不可控制的爆炸式增长和丰富化，给任何用户查找和检索他真正想要在Web上找到的相关信息带来了严峻的挑战。轻松高效地在网站中找到相关网页的过程变得非常困难。这给研究人员开发新的挖掘技术以改善Web上的用户体验带来了许多挑战，也使组织了解客户的真正信息兴趣和需求以通过提供相应的服务来相应地改善其目标服务而带来了许多挑战。真正满足每个在线客户需求的产品，服务和信息。 ud考虑到这些挑战，Web挖掘旨在提取隐藏的模式并从Web页内容，Web超链接和Web使用日志中发现有用的知识。根据挖掘过程中使用的主要Web数据类型，Web挖掘任务可以分为三种主要类型：Web内容挖掘，它使用文本挖掘技术从Web页面内容中提取知识； Web结构挖掘，它从文本挖掘技术中提取模式。代表网站结构的超链接，以及Web用法挖掘，后者从Web服务器日志中挖掘用户的Web导航模式，该日志记录了每个用户进行的Web页面访问，代表了用户与网站中Web页面之间的交互活动。本文的主要目的是通过提出和开发新颖的基于Web挖掘的方法，为应对因信息爆炸和Web过载而造成的挑战做出贡献。为了实现这一目标，本文提出，分析和评估了三个主要方面。首先，开发一种集成的Web结构和使用情况挖掘方法，该方法建议将要放置在该网站主页上的网站浏览者的超链接集合。其次，开发了集成的Web内容和用法挖掘方法，以增进对用户Web行为的理解并发现用户对网站的兴趣。第三，开发基于最新社交网络概念（例如标签云）的监督分类模型，以改善从Web社交博客中检索相关文章和帖子的过程。

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    Ammari Ahmad N.;

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  • 年度 2010
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