共查询到19条相似文献，搜索用时 109 毫秒
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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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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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本文主要研究了查询语义树的生成策略、用户查询语义的提取机制，以及查询语义树中语义边界的确定方法。通过查询语义树产生候选扩展词，再计算候选扩展词与所有查询项在初检局部文档集合中的共现度，用以评估扩展词质量，使得扩展词与用户查询所蕴涵的主题具有较强的语义相关性。实验结果表明，与传统向量空间模型检索算法比较，查询性能有明显的改善。 相似文献
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以物流业务为导向,根据Web Service和本体构建了一个基于语义Web的供应商管理系统。在领域本体的基础上,以用户静态和动态偏好相结合来扩展用户的查询条件,从而实现了根据用户的个性需要和货物的属性来匹配到满足条件的供应商,提高了查全查准率。基于语义Web的供应商管理系统可以更有效的配置物流资源以及更合理的个性化服务。 相似文献
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基于主题偏好的个性化检索模型研究 总被引：1，自引：0，他引：1
随着互联网信息资源日益增多,个性化检索成为了信息检索领域的研究热点.传统的个性化检索利用网页内容形成的向量空间模型来描述用户兴趣,使得用户的查询响应较慢,修正用户兴趣计算量大.由此提出基于主题偏好的个性化检索模型,用户兴趣由用户的主题偏好来表示,结合主题敏感的PageRank算法对检索结果排序.旨在更好地体现用户兴趣,并简化计算,减少查询响应时间. 相似文献
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[目的/意义] 移动视觉搜索是智慧图书馆知识服务创新的重要内容。移动环境下,根据动态变化的情境推断用户意图,为用户提供合适的资源是智慧型知识服务的必然要求。[方法/过程] 在分析融合情境的智慧图书馆移动视觉搜索服务模型构建动因的基础上,归纳模型的内在特征,对模型体系框架和关键问题进行了设计和论述,并提出相应的技术要点。[结果/结论] 将情境计算应用于移动视觉搜索服务中,是弥补语义鸿沟、提高查询相关度和用户满意度的有效途径。该研究可为智慧图书馆个性化知识服务的优化提供参考。 相似文献
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《Information processing & management》2014,50(4):599-620
Query enrichment is a process of dynamically enhancing a user query based on her preferences and context in order to provide a personalized answer. The central idea is that different users may find different services relevant due to different preferences and contexts. In this paper, we present a preference model that combines user preferences, user context, domain knowledge to enrich the initial user query. We use CP-nets to rank the preferences using implicit and explicit user preferences and domain knowledge. We present some algorithms for preferential matching. We have implemented the proposed model as a prototype. The initial results look promising. 相似文献
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Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments. 相似文献
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Interactive query expansion (IQE) (c.f. [Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121–187]) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers’ established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as Real-Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used – and when it may be useful – we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE interactively. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion. 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
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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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The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. The first method consists of asking the user to select a number of sentences that represent documents. The second method consists of showing to the user a list of noun phrases extracted from the initial document set. Both methods then expand the query based on the user feedback. The TREC results show that one of the methods is an effective means of interactive query expansion and yields significant performance improvements. The paper presents a comparison of the methods and detailed analysis of the evaluation results. 相似文献