共查询到18条相似文献，搜索用时 218 毫秒
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基于浏览行为和浏览内容的用户兴趣建模 总被引：4，自引：0，他引：4
面对因特网的海量信息，为了更好地实现基于用户兴趣的个性化信息服务，提出一种隐式地获取用户兴趣模型和更新用户兴趣模型的方法。这种方法不需要用户显式地提供兴趣信息，只需要用户浏览页面时的动作和浏览的内容来获取有用的信息，随后利用这些信息建立和更新用户兴趣模型。该模型能较好地描述用户的兴趣类型及兴趣度，提高个性化信息服务的效率。 相似文献
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搜索引擎个性化检索研究综述 总被引：3，自引：0，他引：3
搜索引擎个性化检索是搜索引擎主动或被动地搜集用户的偏好、兴趣等信息需求特征,建立用户模型,检索并反馈与用户特定需求密切相关结果的过程。论述了搜索引擎个性化检索中的用户模型和网页排名技术的研究。在分析大量研究成果基础上,总结了存在的不足并对未来的研究趋势进行探讨。 相似文献
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数字图书馆个性化信息检索模型研究* 总被引：3，自引：0，他引：3
许春漫 《现代图书情报技术》2006,1(3):15-19
结合向量空间技术、Agent技术、Web日志挖掘等技术提出了一个基于概念的数字图书馆个性化信息检索模型。该模型根据用户主动提供的初始信息建立基于概念的用户兴趣模型，利用用户对文档的主动评价和用户的访问行为更新用户兴趣模型，并将用户兴趣模型用于检索结果的相关度排序和最新信息的推荐以及合作推荐。最后给出系统的实现方法。 相似文献
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面对因特网的海量信息,为了更好地实现基于用户兴趣的个性化信息服务,提出一种隐式地获取并更新用户兴趣模型的方法利用用户模型捕捉用户的点击历史信息如何同其兴趣相关;基于用户模型的学习模型通过学习用户的点击历史数据来标识用户的个人兴趣;通过学习到的用户喜好信息来对搜索结果予以再排序,从而实现个性化搜索信息呈现.设计了用户兴趣学习算法与个性化排序算法.实时数据实验显示,即使在用户兴趣主题数增加的情况下,本方法仍能较好地描述用户的兴趣类型及兴趣度,提高个性化信息服务的质量与效率. 相似文献
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要实现网络信息或数字图书馆信息的有效多语言获取,需充分考虑用户交互.通过用户实验,检验用户相关反馈机制在多语言信息获取中的作用,并分析用户行为特点.实验结果证明,查询扩展、翻译优化以及两者的结合均是有效的用户相关反馈方法. 相似文献
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使用学习模型定量分析信息用户检索行为特征,对于扩展传统用户研究的方法、促进学科间融合具有重要意义。本文首先分析了信息用户检索决策中的强化学习特征,并选择了强化学习模型中的Bush-Mosteller模型和Borgers-Sarin模型对用户的检索决策行为形成过程进行模拟。模拟结果表明Borgers-Sarin模型成功捕捉了用户检索行为的形成过程,说明用户学习规则特征表现为将预期与反馈结果进行比较;模型的参数表明用户行为的黏滞性、惯性特征明显。 相似文献
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针对个性化搜索的3个关键问题：用户信息搜集，用户信息库的动态更新与个性化检索算法，探索性地提出基于Ajax用户行为跟踪方案，以会话为单位动态更新用户行为信息库策略与加入用户文档的向量空间检索模型，并在此基础上设计和实现个性化搜索引擎实验系统。 相似文献
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基于用户行为的全文检索系统个性化研究 总被引：1，自引：0，他引：1
总结国内有关检索系统个性化研究的现状并进行分析,针对全文检索系统个性化服务方面存在的不足提出了基于用户行为全文检索系统模型,阐释了模型中的三个关键问题,包括相关反馈行为评价体系的制定、用户显式隐式行为的获取、用户兴趣建模和基于行为的相关度算法优化,最后列举了基于用户行为的全文检索系统可提供的四项个性化服务内容,包括个性化用户界面、优化检索策略、个性化检索结果、个性化推荐. 相似文献
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We propose a method for performing evaluation of relevance feedback based on simulating real users. The user simulation applies
a model defining the user’s relevance threshold to accept individual documents as feedback in a graded relevance environment;
user’s patience to browse the initial list of retrieved documents; and his/her effort in providing the feedback. We evaluate
the result by using cumulated gain-based evaluation together with freezing all documents seen by the user in order to simulate
the point of view of a user who is browsing the documents during the retrieval process. We demonstrate the method by performing
a simulation in the laboratory setting and present the “branching” curve sets characteristic for the presented evaluation
method. Both the average and topic-by-topic results indicate that if the freezing approach is adopted, giving feedback of
mixed quality makes sense for various usage scenarios even though the modeled users prefer finding especially the most relevant
documents. 相似文献
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As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining
information that accurately matches user information needs. A number of factors affect information retrieval effectiveness
(the accuracy of matching user information needs against the retrieved information). First, users often do not present search
queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often
highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats
and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval
effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational
intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative
(subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An
information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall. 相似文献
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In this paper, we present a framework that can process a user query for retrieval of information from documents of different properties across multiple domains, with specific application to patent laws and regulations. The framework has three basic components. The first component is ontology mapping and generation. What happens is that the keywords entered by users are mapped into a subset of relevant keywords. This step is performed by looking up those words in an ontology database. The second component is the joint and cross search in various document domains; in our case, they are patents and scientific publications. The last component is to modify the search results by applying user feedback statistics. The results of feedback will be saved as metadata for future uses.A case example is given to demonstrate how results from multiple domain searches can be combined using ontology and cross referencing. We use an example of well-known biotechnology patents on erythropoietin (EPO) and give detailed analysis on each document domain with this keyword. Relationships between each domain are demonstrated.A user feedback mechanism is also discussed in this paper. The ability to take user feedback into the framework is important. There is no doubt that domain knowledge from expert or experienced users could be a very good compliment to the proposed system. Both direct and indirect user feedbacks are discussed. 相似文献
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交互式跨语言信息检索是信息检索的一个重要分支。在分析交互式跨语言信息检索过程、评价指标、用户行为进展等理论研究基础上,设计一个让用户参与跨语言信息检索全过程的用户检索实验。实验结果表明:用户检索词主要来自检索主题的标题;用户判断文档相关性的准确率较高;目标语言文档全文、译文摘要、译文全文都是用户认可的判断依据;翻译优化方法以及翻译优化与查询扩展的结合方法在用户交互环境下非常有效;用户对于反馈后的翻译仍然愿意做进一步选择;用户对于与跨语言信息检索系统进行交互是有需求并认可的。用户行为分析有助于指导交互式跨语言信息检索系统的设计与实践。 相似文献
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The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using number-to-view graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones. 相似文献
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基于Ontology的个性化检索 总被引：4，自引：0，他引：4
目前检索工具的设计大都面向所有用户，而不考虑用户个人的特殊信息需求。本文提出一种基于Ontology的个性化检索方法，该方法自动学习用户查询的历史记录，构建用户兴趣模型，以此推导用户新提问的真正意图，满足用户特殊的信息需求。该方法适用于Internet特定领域或者特定用户群、企业网等智能信息检索。 相似文献
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认知建构视角下交互式信息检索模型研究 总被引：1，自引：0，他引：1
[目的/意义]信息检索本质上是一个认知过程,研究促进用户认知的交互式信息检索模型具有重要意义。[研究设计/方法]以建构主义理论为指导,以促进用户的认知发展为研究目标,构建了以信息空间层、用户空间层和界面交互层为顶层分析框架的交互式信息检索模型,并开发了原型系统。[结论/发现]实验结果表明原型系统能有效地促进用户对信息空间的探索与挖掘,帮助用户积极主动地进行认知建构,发展认知空间。[创新/价值]将认知建构理论运用于信息检索领域,从交互设计方面对检索系统提出了改进建议,以更好地提供认知支持。 相似文献