【作者】 李连江；

【导师】 张健沛；

【作者基本信息】 哈尔滨工程大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 通过使用搜索引擎,人们可以方便快捷的从大量信息中查找出自己需要的内容。比起曾经功能单一的搜索引擎,现在的搜索引擎已经有了很大的发展。但是,现有的搜索引擎技术仍然存在有不够智能化,不能够在大量的搜索结果中挑选出用户真正感兴趣的结果的问题。而这正是本课题要研究改进的问题。针对用户对搜索引擎个性化服务的需要,作者阐述了一种个性化搜索引擎页面排序算法的实现思想:采用基于Web数据挖掘的方法从用户动作中判断用户是否对网页有“兴趣”;在对原有搜索引擎排序技术进行研究与分析的基础上,采用聚类的方法对网页进行分类;建立用于存储用户兴趣信息的关键字——用户兴趣表,同时建立了网页类型表作为支撑;通过分析国内外关于个性化搜索的著作,提出一种适合个性化排序的权值计算公式,通过对存储在用户兴趣表中的用户兴趣信息进行分析得到符合用户兴趣的排序结果。同时,基于这个排序算法本文建立了一种个性化搜索引擎模型,并对各部分的实现进行分析设计。在模型中加入个性化分析模块以及网页类型分析模块,目的是提高搜索引擎的个性化分析能力,使搜索结果更符合用户需要,提高用户对个性化搜索引擎的满意度。最后,作者通过对比传统搜索引擎的实验验证了采用个性化排序算法的搜索引擎模型具备较高的用户满意度。分析了可能存在的问题,并指出可以继续研究的方向。更多还原

【Abstract】 Through search engine, people could easily get the content what they need. Compared with the old one, the search engine today has a large development. But, there are also some problems, for example, the search engine is not intelligent enough, they can not get the really interested answers of the users from amount of searching results. And it is just the attitude of the research.In allusion to the need of search engine’s personalized service, the author puts forward the ranking pages algorithm of personalized search engine: The thesis judged whether the user was interested in the web through the user’s action based on the web data-mining method. The author cheese clustering to class the webpage, based on the analysis for original search engine technology. The thesis build on a Key-word and User-interest table for the User-interest message’s storing, and build on a web-type table to support the User-interest table. Through analyzed the works, the author give a rank formula which could get the proper result through the User-interest message storing in the User-interest table. At the same time, this thesis build up a model of personalized search engine and the realization of each part of the system are analyzed and designed. The purpose for adding personalized and page-type analyzing model is to improve the personalized analyzing ability, to make the searching results conform users’ need and improve users’ satisfaction for the personalized search engine.At last, the author has confirmed the better users’ satisfaction of the model by experiments compared with the traditional search engine. Also, the author brings forward the direction of the next step of research and some potential problems.更多还原