一种基于常用搜索引擎的智能信息检索系统

针对用户利用常用搜索引擎查询信息时，搜索引擎返回海量杂乱、无序的网页，用户难以从中快速、准确地获得真正关心的信息的现状，从Internet用户的兴趣度出发，设计了一种基于近似网页聚类算法的智能搜索系统。该系统在用户利用常用搜索引擎系统进行信息检索时，消除搜索引擎返回的重复页，对剩余页面进行聚类，返回给用户聚类后的网页簇，这样用户就可以选择浏览自己感兴趣的页面，从而大大提高了信息检索的查准率；实验证明该系统在保证查全率和查准率的基础上大大提高了搜索效率。     

针对小文本的Web数据挖掘技术及其应用

现有搜索引擎技术返回给用户的信息太多太杂,为此提出一种针对小文本的基于近似网页聚类算法的Web文本数据挖掘技术,该技术根据用户的兴趣程度形成词汇库,利用模糊聚类方法获得分词词典组,采用MD5算法去除重复页面,采用近似网页聚类算法对剩余页面聚类,并用马尔可夫Web序列挖掘算法对聚类结果排序,从而提供用户感兴趣的网页簇序列,使用户可以迅速找到感兴趣的页面。实验证明该算法在保证查全率和查准率的基础上大大提高了搜索效率。由于是针对小文本的数据挖掘,所研究的算法时间和空间复杂度都不高,因此有望成为一种实用、有效的信息检索技术。     

实例学习在搜索引擎中的研究与应用

提出一种解决信息检索中信息过载问题的方案.通过对用户搜索习惯分析,发现用户对网页的选取主要依据搜索返回的网页摘要信息.分析摘要信息,运用人工智能中实例学习理论,推断用户的搜索目的.通过实例证明,该方案应用于搜索引擎,可以提高搜索引擎的查准率和智能性.     

基于聚类和用户兴趣分析结合的个性化元搜索

随着Web信息的快速增长,搜索引擎已成为用户信息检索的主要工具。元搜索引擎综合了多个搜索引擎的搜索结果,提高了搜索的覆盖率,但是返回的结果往往数目庞大,并且很多结果与用户查询并不相关,这直接影响了用户检索的质量并增加了用户检索的代价。本文提出一种基于聚类的个性化元搜索引擎模型,系统通过对用户建立兴趣模型,对此模型进行聚类形成不同用户群,并对检索到的结果进行聚类处理,与用户模型聚类相结合返回给用户个性化的搜索结果。     

基于聚类的个性化元搜索引擎设计

Internet上信息资源的飞速膨胀造成用户在进行信息检索时的不便,传统的搜索引擎不能很好地解决这个问题。因此提出了一种基于聚类的个性化元搜索引擎模型,系统通过对用户建立个人模型,对此模型进行聚类形成不同用户群,并对检索到的结果进行聚类处理,同用户模型聚类相结合返回给用户个性化的搜索结果。分析了个性化元搜索引擎的系统构成,详细介绍了每个模块的功能,最后展望了它的发展前景。     

用元搜索引擎实现刑罚信息个性化检索

针对搜索引擎存在的问题和刑罚信息检索的特定需要,提出了一个元搜索引擎模型,通过元搜索引擎为用户提供统一的访问服务,运用聚类方法对搜索引擎的返回结果进行快速分类,并通过用户的行为反应探视用户的兴趣,动态调整聚类结果,帮助用户快速检索到相关信息.提高了检索的有效性、查准率和查全率,实现了刑罚信息的个性化检索.     

基于搜索结果的Web预取模型研究

目前搜索引擎返回的信息太多且难以根据用户的兴趣提供检索结果,而个性化推荐是一种旨在减轻用户在信息检索方面负担的有效方法.文中把内容过滤技术和文档聚类技术相结合,以改进的STC聚类方法组织搜索结果,主动推荐用户感兴趣的文档并将其中的Top-N对象预取到本地. WWW缓存中的Web文档代表了用户当前的兴趣,通过建立用户概率兴趣模型,在搜索结果STC聚类的基础上进行内容过滤.实验表明,基于搜索结果的Web预取模型具有较好的时间性能和较高的查准率.     

一种基于聚类技术的个性化信息检索方法

实践证明聚类技术是改进搜索结果显示方式的一种有效手段。然而,目前的聚类方法没有考虑到用户兴趣,对于相同的查询,返回给所有用户同样的聚类结果。由此提出一种个性化聚类检索方法。该方法改进了k-means算法,利用该算法对传统搜索引擎返回的结果结合用户兴趣进行聚类,返回针对特定用户的网页簇。实验证明该方法能够提供个性化服务,改善了聚类的效果,提高了用户的检索效率。     

基于用户行为聚类的搜索

因特网的飞速发展,网络资源呈爆炸式的增长。信息检索是人们上网的主要目的之一。目前的信息检索领域有许多检索方法与检索工具,为用户检索信息提供了许多途径。但如何利用搜索引擎实现更快更精确的搜索已经成为这一领域的研究热点。在研究现有的几种搜索引擎的基础上,提出了一种基于用户行为聚类的搜索引擎。通过分析不同的用户行为将搜索用户聚类成不同的用户组,为每组用户返回其喜欢的结果,优化查询结果。     

一种高效的网页聚类方法

当前主流的搜索引擎主要是以与用户查询的相关度来顺序返回搜索结果的,用户往往需要花费较长的时间从结果列表中进行选择.为了解决这个问题,针对搜索引擎返回的标题和摘要信息,构造有向图表示,并在此基础上实现了一种高效的网页聚类原型系统(efficient web clustering system,EWCS).该系统将搜索引擎返回的结果按照一定的标准分类呈现给用户,用户选择感兴趣的类别进行浏览,从而较好地满足了用户对查询速度和准确度的需求.试验结果表明该算法具有一定的可行性和较高的准确率.     

用信息-摘要算法提高Web信息检索效率的研究

针对常用搜索引擎返回给用户的信息中包含大量重复网页的缺陷,提出了一种基于信息-摘要算法的去除重复网页算法。由于算法的成熟,该算法易实现,可移植性强。实验证明该算法能有效地去除常用搜索引擎返回的重复网页,从而为Internet用户提高信息检索效率,具有较强的实用价值。     

Mining the Web's link structure

The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an “off-the-shelf” database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated     

Improving search results with data mining in a thematic search engine

The problem of obtaining relevant results in web searching has been tackled with several approaches. Although very effective techniques are currently used by the most popular search engines when no a priori knowledge on the user's desires beside the search keywords is available, in different settings it is conceivable to design search methods that operate on a thematic database of web pages that refer to a common body of knowledge or to specific sets of users. We have considered such premises to design and develop a search method that deploys data mining and optimization techniques to provide a more significant and restricted set of pages as the final result of a user search. We adopt a vectorization method based on search context and user profile to apply clustering techniques that are then refined by a specially designed genetic algorithm. In this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments that has been run on test sets of web pages.     

Personalized mining of web documents using link structures and fuzzy concept networks

Personalized search engines are important tools for finding web documents for specific users, because they are able to provide the location of information on the WWW as accurately as possible, using efficient methods of data mining and knowledge discovery. The types and features of traditional search engines are various, including support for different functionality and ranking methods. New search engines that use link structures have produced improved search results which can overcome the limitations of conventional text-based search engines. Going a step further, this paper presents a system that provides users with personalized results derived from a search engine that uses link structures. The fuzzy document retrieval system (constructed from a fuzzy concept network based on the user's profile) personalizes the results yielded from link-based search engines with the preferences of the specific user. A preliminary experiment with six subjects indicates that the developed system is capable of searching not only relevant but also personalized web pages, depending on the preferences of the user.     

基于搜索引擎的双语混合网页识别新方法

该文提出了一种从搜索引擎返回的结果网页中获取双语网页的新方法,该方法分为两个任务。第一个任务是自动地检测并收集搜索引擎返回的结果网页中的数据记录。该步骤通过聚类的方法识别出有用的记录摘要并且为下一个任务即高质量双语混合网页的验证及其获取提供有效特征。该文中把双语混合网页的验证看作是有效的分类问题,该方法不依赖于特定领域和搜索引擎。基于从搜索引擎收集并经过人工标注的2 516条检索结果记录,该文提出的方法取得了81.3%的精确率和94.93%的召回率。     

Tuning up the search engine

Advances in Internet search engine technology may not help you blast Klingons into outer space, but they should help you find them more quickly on the Web. The whole arena for Internet searching has become rather interesting. Search engines appear poised to make some serious breakthroughs in relevancy ranging and personalization that promise to increase the accuracy and reliability of search. On the ether hand, data suggests that users are becoming increasingly disenchanted with search engines that don't actually search the Web, but rather search records of the Web sites their robots have visited. Some online merchants (Victoria's Secret, for example) don't even enable keyword searches on their sites. The Web's increasingly dynamic nature complicates searching. New pages created on the fly using personalization information, and even static content, with dynamically inserted sidebars, navigation bars, advertising and commentary, can present a rapidly changing picture for any robot to discover. And as indexes grow larger, search system performance becomes a significant problem     

基于Lucene的中文全文检索系统的研究与设计

提出了一种基于Lucene的中文全文检索系统模型.通过分析Lucene的系统结构,系统采用了基于统计的网页正文提取技术,并且加入了中文分词模块和索引文档预处理模块来提高检索系统的效率和精度.在检索结果的处理上,采用文本聚类的办法,使检索结果分类显示,提高了用户的查找的效率.实验数据表明,该系统在检索中文网页时,在效率,精度和结果处理等方面性能明显提高.