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Searching and mining the Web for personalized and specialized information.

机译:在Web上搜索和挖掘个性化和专用信息。

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摘要

With the rapid growth of the Web, users are often faced with the problem of information overload and find it difficult to search for relevant and useful information on the Web. Besides general-purpose search engines, there exist some alternative approaches that can help users perform searches on the Web more effectively and efficiently. Personalized search agents and specialized search engines are two such approaches. The goal of this dissertation is to study how machine learning and artificial intelligence techniques can be used to improve these approaches.; A system development research process was adopted as the methodology in this dissertation. In the first part of the dissertation, five different personalized search agents, namely CI Spider, Meta Spider, Cancer Spider, Nano Spider, and Collaborative Spider, were developed. These spiders combine Web searching with various techniques such as noun phrasing, text clustering, and multi-agent technologies to help satisfy users' information needs in different domains and different contexts. Individual experiments were designed and conducted to evaluate the proposed approach and the experimental results showed that the prototype systems performed better than or comparable to traditional search methods.; The second part of the dissertation aims to investigate how artificial intelligence techniques can be used to facilitate the development of specialized search engines. A Hopfield Net spider was proposed to locate from the Web URLs that are relevant to a given domain. A feature-based machine-learning text classifier also was proposed to perform filtering on Web pages. A prototype system was built for each approach. Both systems were evaluated and the results demonstrated that they both outperformed traditional approaches.; This dissertation has two main contributions. Firstly, it demonstrated how machine learning and artificial intelligence techniques can be used to improve and enhance the development of personalized search agents and specialized search engines. Secondly, it provided a set of tools that can facilitate users in their Web searching and Web mining activities in various contexts.
机译:随着Web的快速发展,用户经常面临信息过载的问题,并且很难在Web上搜索相关和有用的信息。除了通用搜索引擎之外,还有一些替代方法可以帮助用户更有效地在Web上执行搜索。个性化搜索代理和专用搜索引擎就是两种这样的方法。本文的目的是研究如何使用机器学习和人工智能技术来改进这些方法。本文采用系统开发研究方法作为方法论。在论文的第一部分中,开发了五个不同的个性化搜索代理,分别是CI蜘蛛,Meta蜘蛛,Cancer蜘蛛,Nano蜘蛛和协作蜘蛛。这些蜘蛛将Web搜索与各种技术(例如名词短语,文本聚类和多智能体技术)相结合,以帮助满足用户在不同域和不同上下文中的信息需求。设计并进行了单独的实验,以评估所提出的方法,实验结果表明,原型系统的性能优于或可与传统搜索方法媲美。论文的第二部分旨在研究如何利用人工智能技术来促进专业搜索引擎的发展。提出了Hopfield网络蜘蛛,以从与给定域相关的Web URL进行定位。还提出了一种基于特征的机器学习文本分类器,以对网页进行过滤。为每种方法构建了原型系统。对两个系统都进行了评估,结果表明它们都优于传统方法。本论文有两个主要贡献。首先,它展示了如何使用机器学习和人工智能技术来改善和增强个性化搜索代理和专用搜索引擎的开发。其次,它提供了一套工具,可以帮助用户在各种情况下进行Web搜索和Web挖掘活动。

著录项

  • 作者

    Chau, Michael Chiu-Lung.;

  • 作者单位

    The University of Arizona.;

  • 授予单位 The University of Arizona.;
  • 学科 Business Administration Management.; Information Science.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 237 p.
  • 总页数 237
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 贸易经济;信息与知识传播;
  • 关键词

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