基于改进用户浏览行为量化分析的兴趣网页获取

在大量的Web个性化服务模型或系统中,用户兴趣模型均是通过挖掘用户浏览历史网页获得的。因此从大量的浏览历史里获取用户兴趣网页对于Web个性化服务模型或系统十分重要。该文通过对用户浏览行为进行量化分析来判断兴趣网页,目的是为后续的用户兴趣建模提供准确的挖掘对象。在原有量化分析方法的基础上,该文对浏览行为的贡献值进行归一化,减少需要确定的参数,在一定程度上提高了算法的运行效率,使算法具有更好的可行性。     

改进的关联规则算法在电子商务中的应用

Web日志数据中保存有大量用户访问信息,而Web日志挖掘就是对系统日志信息以及用户的注册数据等进行挖掘,以发现有用的模式和知识。首先介绍了Web日志挖掘的基本流程,然后介绍了电子商务中的日志挖掘,并着重分析了在模式识别中如何利用改进的关联规则算法来挖掘出用户频繁访问的路径和页面兴趣度,为个性化推荐系统模型提供了依据,从而证实了对Web日志数据进行挖掘具有很重要的现实意义。     

基于WWW缓存的用户长期兴趣发现

建立用户兴趣模型是实现个性化服务的关键技术之一.利用Web挖掘的方法,针对用户的兴趣变化,结合用户浏览Web页面的日期和相应Web页面特征项的词频,来建立用户长期和短期兴趣,并且通过模拟实验,验证该方法的有效性.     

序列模式挖掘在电子商务个性化服务中的应用

分析了电子商务发展面临的问题和个性化服务的特点,提出了Web使用挖掘技术在电子商务个性化服务中的应用方法,论述了基于Web挖掘的个性化服务研究,详细阐述了其挖掘过程,最后讨论了使用序列模式和分类相结合的技术得以实现个性化服务的方法。利用这些算法得到的个性化信息可以准确把握用户兴趣模式并对Web信息资源的组织方式进行有效更新,从而提高网络信息服务效率,为用户提供“一对一”的具备自适应性的智能个性化服务。     

序列模式挖掘在电子商务个性化服务中的应用

分析了电子商务发展面临的问题和个性化服务的特点，提出了Web使用挖掘技术在电子商务个性化服务中的应用方法，论述了基于Web挖掘的个性化服务研究．详细阐述了其挖掘过程，最后讨论了使用序列模式和分类相结合的技术得以实现个性化服务的方法。利用这些算法得到的个性化信息可以准确把握用户兴趣模式并对Web信息资源的组织方式进行有效更新，从而提高网络信息服务效率，为用户提供“一对一”的具备自适应性的智能个性化服务。     

一个个性化的Web信息采集模型

介绍了个性化技术和个性化Web信息的采集技术,重点分析了个性化的Web信息采集模型,包括系统总体结构、用户兴趣的获取、个性化Web信息采集流程、个性化推荐的实现。最后对个性化Web信息采集与搜索引擎作了一个比较,分析了个性化Web信息采集的应用。     

关联规则算法在Web个性化服务中的研究与应用

重点研究了Web日志挖掘,提出了一个Web个性化信息挖掘模型,设计了某高校图书馆个性化服务系统My Library。系统采用关联规则挖掘算法,从服务器日志中得到用户感兴趣的隐式模式,并将该隐式兴趣集推荐给用户,从而在一定程度上实现了个性化服务。     

WMS:面向领域的Web信息挖掘系统

研究并实现了一个面向领域的Web挖掘系统WMS，能有效地帮助用户挖掘Web上的信息和知识，用户可以通过提交Web页面、文本文档、URLs或关键词，向系统表达自己希望获得的信息主题，系统自动学习用户对特定领域的兴趣．并依据用户对系统采集文档的反馈评估，不断自适应地调整用户兴趣模型．WMS依据用户兴趣模型，利用智能Agents，对用户感兴趣的有关信息进行搜索和过滤，并对主要相关Web站点的信息更新进行监测，利用人工神经网络和智能Agents技术，WMS对所积累的文档库进行信息和知识挖掘，并自动将新信息推荐给用户．     

一种以Web语义挖掘的个性化信息推荐设计

该文首先介绍了介绍Web知识挖掘的实现流程和数据挖掘的基本原理及方法,通过对Web知识的分析,引出基于Web挖掘的个性化信息推荐流程.然后研究了基于语义层次Web的个性化信息推荐的方法包括用户兴趣的感知方法、用于兴趣的捕获方法等,在此基础之上,利用导出语义层次的Web使用文档和生成个性化推荐的Web页面集,并详细介绍了...     

Web挖掘在个性化远程教育中的应用

基于Web挖掘，提出了一种新的个性化远程教育模型。它能充分利用用户Web访问记录，同时结合用户与站点的交互数据进行挖掘，以此来发现学习者的浏览（学习）兴趣，从而改进页面的设计，优化站点结构，更好地满足学习者的个性化需求，提升个性化远程教育的质量。     

Towards a multimedia World-Wide Web information retrieval engine

Search engines are useful because they allow the user to find information of interest from the World-Wide Web. However, most of the popular search engines today are textual; they do not allow the user to find images from the Web. This paper describes a search engine that integrates text and image search. One or more Web sites can be indexed for both textual and image information, allowing the user to search based on keywords or images or both. Another problem with the current search engines is that they show the results as pages of scrolled list; this is not very user-friendly. Therefore our search engine allows the user to visualize the results in various ways. This paper explains the indexing and searching techniques of the search engine and highlights several features of the querying interface to make the retrieval process more efficient. Examples are used to show the usefulness of the technology.     

AMORE: A World Wide Web image retrieval engine

Search engines are useful because they allow the user to find information of interest from the World Wide Web. However, most of the popular search engines today are textual; they do not allow the user to find images from the Web. This paper describes AMORE, a Web search engine that allows the user to retrieve images from the Web by specifying relevant keywords or a similar image. Text and image search can also be combined. Moreover, we have developed a Query Result Visualization Environment that allows the organization of the results if many images are retrieved. In this paper we present AMORE's user interface and explain the technique for retrieving images visually similar to a user specified image. The method of automatically assigning relevant keywords to the images is then explained. Finally, the architecture of the system as well as some interesting observations of our experiences with AMORE are discussed. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于搜索结果的预取技术研究

搜索引擎往往返回给用户一个包含大量文档片段的列表，用户从中筛选出自己所需要的文档。文中提出一种预取代理的方法：对搜索引擎返回的结果进行聚类分析，使得用户以主题的方式来查看结果，满足用户搜索请求的个性化服务；同时对聚类进行评价，推测出用户可能感兴趣的文档，并将它们预取过来，从而减少网络延迟。     

Performing Binary-Categorization on Multiple-Record Web Documents Using Information Retrieval Models and Application Ontologies

To retrieve Web documents of interest, most of the Web users rely on Web search engines. All existing search engines provide query facility for users to search for the desired documents using search-engine keywords. However, when a search engine retrieves a long list of Web documents, the user might need to browse through each retrieved document in order to determine which document is of interest. We observe that there are two kinds of problems involved in the retrieval of Web documents: (1) an inappropriate selection of keywords specified by the user; and (2) poor precision in the retrieved Web documents. In solving these problems, we propose an automatic binary-categorization method that is applicable for recognizing multiple-record Web documents of interest, which appear often in advertisement Web pages. Our categorization method uses application ontologies and is based on two information retrieval models, the Vector Space Model (VSM) and the Clustering Model (CM). We analyze and cull Web documents to just those applicable to a particular application ontology. The culling analysis (i) uses CM to find a virtual centroid for the records in a Web document, (ii) computes a vector in a multi-dimensional space for this centroid, and (iii) compares the vector with the predefined ontology vector of the same multi-dimensional space using VSM, which we consider the magnitudes of the vectors, as well as the angle between them. Our experimental results show that we have achieved an average of 90% recall and 97% precision in recognizing Web documents belonged to the same category (i.e., domain of interest). Thus our categorization discards very few documents it should have kept and keeps very few it should have discarded.     

MAPBOT: a Web based map information retrieval system

Many types of information are geographically referenced and interactive maps provide a natural user interface to such data. However, map presentation in geographical information systems and on the Web is closed related to traditional cartography and provides a very limited interactive experience. In this paper, we present MAPBOT, an interactive Web based map information retrieval system in which Web users can easily and efficiently search geographical information with the assistance of a user interface agent (UIA). Each kind of map feature such as a building or a motorway works as an agent called a Maplet. Each Maplet has a user interface level to assist the user to find information of interest and a graphic display level that controls the presence and the appearance of the feature on the map. The semantic relationships of Maplets are defined in an Ontology Repository provided by the system which is used by the UIA to assist a user to semantically and efficiently search map information interested. An Ontology Editor with a graphic user interface has been implemented to update the Ontology Repository. Visualization on the client is based on Scalable Vector Graphics which provides a high quality Web map.     

Modeling user interests by conceptual clustering

As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.     

个性化元搜索引擎的研究与设计

随着Web信息的快速增长和人们对信息检索质量要求的提高,传统的搜索引擎已不能很好地满足人们的需求. 本文提出了一种个性化元搜索引擎模型.个性化是指模型可以针对不同的用户建立不同的用户兴趣模型,然后根据用户兴趣,模型对搜索结果进行过滤、重排序处理,使得显示给用户的搜索结果更具有针对性.本文阐述了各主要功能模块工作原理,并详细介绍了根据用户兴趣模型对搜索结果进行排序的算法,实验表明该算法能够有效地提高用户的检索质量.     

基于Bayes概率的用户兴趣发现

本文结合网页结构，充分考虑用户在网页的滞留时间和页面切换，基于Bayes概率提出了一种能挖掘出优良的用户兴趣迁移模式及感兴趣的页面。采用本文提出的思想及算法，再结合人工智能策略，将能更好地辅助网站设计，并为电子商务的决策提供充分依据。     

个性化搜索引擎

随着网络上的知识不断暴涨，用户要求快速、有效地获取网络资源，该文提出了一种通过产生用户动态偏好来达到个性化搜索的方法，通过对搜索结果作一种基于内容的按名分类算法来为用户创建一个以RDF的形式表达的动态偏好。通过对RDF偏好文件的聚类来发现用户社区，产生相应的用户社区偏好。在搜索过程中，通过对该用户及其所属社区的偏好来分析。对搜索结果按重要性排序，达到了用户个性化搜索的目的。     

基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。