共查询到18条相似文献，搜索用时 171 毫秒
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通过分析用户利用传统搜索引擎进行信息检索所存在的不足,介绍了一种自适应搜索引擎。它能够主动采集用户对搜索结果的访问序列,挖掘出其中隐含的用户兴趣信息,据此来调整检索式,再利用向量空间模型对预搜索结果重新排序,并返回给用户,从而搜索引擎的返回结果可以根据用户的使用情况进行动态自适应调整。 相似文献
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解决用户的模糊查询问题一直以来是信息检索领域研究的热点。为了解决不同用户间的查询差异，一种称为个性化搜索的技术得以提出，其通过获取用户的喜好来识别查询意图，但研究发现很少有用户愿意直接或间接提供个人信息。本文提出一种基于用户点击历史信息自动获取用户兴趣进而对搜索结果进行个性化呈现的Web搜索系统架构。基于主题相关PageRank技术，设计了用户兴趣学习算法和个性化搜索页面排序算法。实验表明该算法能有效学习用户的兴趣信息，提高了个性化Web搜索质量。 相似文献
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个性化搜索引擎是一种用户驱动网页排名结果的优化方式。基于本体和语义网,用户建模可以作出准确的查询结果,它包括：限定搜索方式、过滤搜索结果,以及成为搜索过程等3种方式。因此,个性化搜索引擎用户模型可被视为用户驱动个性化搜索服务的模型。研究结论是整合前人研究并且提出＂用户行为（用户兴趣、用户偏好、用户查询记录）-用户文档（用户行为与关键词组）-用户建模（相关性算法与排名算法）-个性化服务＂的新模型,可作为数字图书馆发展个性化搜索引擎的指引。 相似文献
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搜索引擎搜索结果的评价技术 总被引：6，自引：0，他引：6
搜索引擎根据用户查询在自己的索引数据库中进行查找，并根据相关性分析将查询结果返回给用户。本文就传统信息检索系统的性能效率评价技术,针对Internet的特点，对搜索引擎搜索结果的评价进行了讨论。 相似文献
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第二代搜索引擎模式探析 总被引：17，自引：0，他引：17
习惯上，人们认为网络搜索引擎是查询网站或网页信息的站点或工具，这是一种狭义的定义。广义地说，搜索引擎是指在互联网上或通过互联网能够响应用户提交的搜索请求，返回相应查询结果的信息技术和系统，这里所说…… 相似文献
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目前,常用的全文搜索引擎都是基于关键词检索的,其查准率和查全率都较低,并且返回记录太多,重复信息过多,使得搜索引擎的查询效率很低。基于此,提出了一种基于本体的搜索引擎模型,通过提取的文档中概念,确定其所属的领域本体,以此归类,并用文档—概念匹配系数建立索引。搜索时,采用基于概念匹配的方式进行检索,对属于不同领域本体的文档,分类输出。提高搜索引擎的查准率和查全率,减少冗余信息,从而提高搜索引擎的查询效率。 相似文献
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用户在进行网络搜索时经常会碰到如何使用搜索引擎及进行查询等难题，用户忽略了搜索引擎的使用因其查询信息系统的不同而各异。但我们在指导上网用户如何正确使用搜索引擎方面几何没有进行过研究。本文旨在研究大学生对搜索引擎的理解程度以及帮助用户如何正确使用搜索引擎。 相似文献
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上下文对用户搜索行为的影响 总被引：1，自引：0，他引：1
用户的搜索是在特定搜索上下文中进行的,虽然个性化搜索、社会化搜索可以利用一部分上下文信息,但有时搜索效果因搜索引擎未有效利用其他上下文信息而让人无法接受.论文采用发放问卷调查的方式,探索上下文信息对用户搜索行为的影响.首先针对用户上下文、查询上下文、页面上下文分别设计一定数量的调查题目;其次在新浪等五个网站发放问卷,收集为期一个月的互联网用户反馈结果,得到数据集;最后,分析三类Web上下文信息对用户搜索行为的影响.结果显示:查询上下文影响权重最大、用户上下文次之、页面上下文的影响最小,这一结果可为有效利用上下文信息提供一定的借鉴作用. 相似文献
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在信息检索中对基于用户兴趣的检索结果进行重排得到广泛关注。为了构建用户兴趣的知识库,本文对用户的登录细节和点击数据进行综合分析,提出了定制用户访问信息的方法,同时采用开放式目录项目Dmoz自动进行用户兴趣主题映射,对搜索结果进行个性化分类,并根据用户兴趣对检索结果重排,比正常的搜索引擎更容易找到相关的信息。联机实验结果表明,本文提出的方法可有效地提高用户检索精度。 相似文献
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针对现有搜索引擎的局限性和当前用户的个性化需求,以用户兴趣模型为基础,对个性化元搜索引擎的基本原理和结构、方法及关键技术进行了研究,并在此基础上提出了用户个性化元搜索引擎的简单实现。 相似文献
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如何准确分析用户行为,向用户提供满意的网页信息,一直以来都是个性化信息推荐系统设计的目标。本文在分析现有个性化信息推荐模型的基础上,针对以往研究在推荐兴趣时仅根据语义相关度进行协助性信息推荐,而忽略用户行为规律所包含的潜在兴趣信息的不足,尝试提出一个结合Web语义挖掘和FP-tree规则发现技术的个性化信息推荐模型。该模型利用本体对语义的明确化描述,在挖掘用户行为信息时获取用户兴趣偏好的语义信息,并利用FP-tree技术根据以获取的语义信息推理出用户兴趣行为模式,从而在信息推荐时不仅能准确理解用户兴趣偏好,也能根据用户潜在兴趣规律,推荐给用户更全面的网页信息。 相似文献
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基于本体的数字图书馆个性化信息服务研究 总被引：2，自引：0，他引：2
本文引入本体对信息资源和用户兴趣特征进行描述,提出了在语义层次上实现数字图书馆个性化信息服务的系统框架模型,简单分析了各个部分功能,重点阐述了用户的兴趣建模和更新方法。 相似文献
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As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information. 相似文献
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《Information processing & management》1999,35(3):337-362
There was a proliferation of electronic information sources and search engines in the 1990s. Many of these information sources became available through the ubiquitous interface of the Web browser. Diverse information sources became accessible to information professionals and casual end users alike. Much of the information was also hyperlinked, so that information could be explored by browsing as well as searching. While vast amounts of information were now just a few keystrokes and mouseclicks away, as the choices multiplied, so did the complexity of choosing where and how to look for the electronic information. Much of the complexity in information exploration at the turn of the twenty-first century arose because there was no common cataloguing and control system across the various electronic information sources. In addition, the many search engines available differed widely in terms of their domain coverage, query methods and efficiency.Meta-search engines were developed to improve search performance by querying multiple search engines at once. In principle, meta-search engines could greatly simplify the search for electronic information by selecting a subset of first-level search engines and digital libraries to submit a query to based on the characteristics of the user, the query/topic, and the search strategy. This selection would be guided by diagnostic knowledge about which of the first-level search engines works best under what circumstances. Programmatic research is required to develop this diagnostic knowledge about first-level search engine performance.This paper introduces an evaluative framework for this type of research and illustrates its use in two experiments. The experimental results obtained are used to characterize some properties of leading search engines (as of 1998). Significant interactions were observed between search engine and two other factors (time of day and Web domain). These findings supplement those of earlier studies, providing preliminary information about the complex relationship between search engine functionality and performance in different contexts. While the specific results obtained represent a time-dependent snapshot of search engine performance in 1998, the evaluative framework proposed should be generally applicable in the future. 相似文献
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随着人们对信息获取手段和效率提出越来越高的要求,传统互联网的服务模式正在逐渐向主动式、个性化、高效率的转变。个性化服务技术的出现在一定程度上解决了Internet中信息海量增长与用户获取信息手段相对简单之间的矛盾。用户兴趣建模技术作为个性化服务的核心问题,主要研究如何有效地进行用户兴趣的表示、更新、存储以及计算。 相似文献
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个性化图书推荐主要是以用户特征和借阅行为为挖掘对象,通过获取用户的兴趣特征及隐含的需求模式,实现用户与图书相互关联的个性化图书推荐服务。本文通过挖掘用户的背景信息构建用户特征模型,然后在设计喜好值计算、用户相似度计算和内容相似度计算以及标签信息获取方法的基础上,研究多种不同的图书推荐方法,以挖掘用户的潜在信息需求。最后利用图书馆的真实数据设计面向高校图书馆的个性化图书推荐系统,同时以标准网络数据集通过实验验证来评估推荐方法的有效性。 相似文献
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Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献