一种个性化查询扩展方法

针对当前主流web搜索引擎存在信息检索个性化效果差和信息检索的精确率低等缺点, 通过对已有方法的技术改进, 介绍了一种基于用户历史兴趣网页和历史查询词相结合的个性化查询扩展方法。当用户在搜索引擎上输入查询词时,能根据学习到的当前用户兴趣模型动态判定用户潜在兴趣和计算词间相关度,并将恰当的扩展查询词组提交给搜索引擎,从而实现不同用户输入同一查询词能返回不同检索结果的目的。实验验证了算法的有效性,检索精确率也比原方法有明显提高。     

基于跨语言词向量模型的蒙汉查询词扩展方法研究

跨语言信息检索指以一种语言为检索词,检索出用另一种或几种语言描述的一种信息的检索技术,是信息检索领域重要的研究方向之一。近年来,跨语言词向量为跨语言信息检索提供了良好的词向量表示,受到很多学者的关注。该文首先利用跨语言词向量模型实现汉文查询词到蒙古文查询词的映射,其次提出串联式查询扩展、串联式查询扩展过滤、交叉验证筛选过滤三种查询扩展方法对候选蒙古文查询词进行筛选和排序,最后选取上下文相关的蒙古文查询词。实验结果表明: 在蒙汉跨语言信息检索任务中引入交叉验证筛选方法对信息检索结果有很大的提升。     

个性化的社会标签查询扩展技术研究

随着互联网上的信息日益增长,个性化的搜索需求越来越迫切,由于用户兴趣的不同和行为的差异,如何为不同的用户提供不同的检索结果成为一个具有挑战性的问题。首先对现有搜索引擎的个性化信息检索和查询扩展技术进行了分类总结,分析了它们各自的优缺点。然后提出了基于社会化标签的个性化查询词扩展方法。这些方法通过从用户所收藏的社会化标签或标签所对应的网页中提取出和用户查询词相关的词,来对用户的初始查询进行扩展。最后利用Delicious网站上的用户数据,对比研究了这几种个性化查询扩展算法。通过与Google进行对比分析实验,结果表明所提出的社会化标签的个性化查询词扩展方法能够较好地满足用户的个性化需求,检索结果比Google的检索结果更接近用户需求。     

用于信息检索的查询分析与优化算法

针对信息检索中查询与查询所得文档的关键字不匹配这一个主要的问题,本文提出了用于信息检索的查询分析与优化算法。首先从网络语料库中提取相关词,然后通过计算相关词的重要性,找出附加的备选查询词,最后把这两部分的词都用于信息检索。这种算法能够提供查询的准确度。     

基于潜在语义分析的个性化查询扩展模型

为在信息检索系统中获得更好的查询效果,提出一种混合的个性化查询扩展模型。通过潜在语义分析建立潜在语义空间,并在潜在语义空间中计算得到查询的概念相关扩展词和兴趣相关扩展词,不但有效解决了词典问题,而且满足了不同用户需求多样性和用户多兴趣点的需求。实验表明,该算法能够较好地提高搜索引擎系统的查全率、查准率,以及信息检索效率。     

基于TF-IQF模型和图聚类的个性化搜索研究

针对信息检索领域存在的用词歧义和检索词简短的问题,本文提出了一种基于TF-IQF模型和图聚类的个性化查询建议方法。对于用户的查询请求,提供查询建议,帮助用户进行查询修正,进而检索到其所需的信息;同时通过获取不同用户的查询偏好,以达到个性化查询推荐的目的。实验结果表明,该方法能够给出个性化的查询建议,为用户提供潜在感兴趣的资源,具有较高的准确率。     

一种基于排序学习方法的查询扩展技术

查询扩展作为一门重要的信息检索技术,是以用户查询为基础,通过一定策略在原始查询中加入一些相关的扩展词,从而使得查询能够更加准确地描述用户信息需求。排序学习方法利用机器学习的知识构造排序模型对数据进行排序,是当前机器学习与信息检索交叉领域的研究热点。该文尝试利用伪相关反馈技术,在查询扩展中引入排序学习算法,从文档集合中提取与扩展词相关的特征,训练针对于扩展词的排序模型,并利用排序模型对新查询的扩展词集合进行重新排序,将排序后的扩展词根据排序得分赋予相应的权重,加入到原始查询中进行二次检索,从而提高信息检索的准确率。在TREC数据集合上的实验结果表明,引入排序学习算法有助于提高伪相关反馈的检索性能。     

一种基于相关反馈的信息检索模型

针对现有信息检索系统难以按查询需求处理检索文档的问题,提出了一种基于相关反馈的信息检索模型,分析了查询词分解,推导了相关反馈机制和正规化过程,并进一步阐述了文档提取方法。提出的模型通过相关反馈和查询词扩展,克服了传统方法无法计算文档与查询词之间的相似度问题,并能有效地处理检索文档。仿真结果证明了该模型的有效性和可行性。     

一种基于上下文的中文信息检索查询扩展

在中文信息检索的研究和实践中,由于查询中所使用的词可能与文件集中使用的词不匹配而导致一些相关的文件不能被成功地检索出来,这是影响检索效果的一个很关键的问题。查询扩展可以在一定程度上解决这种词的不匹配现象,然而,实验表明,通常简单的查询扩展并不能稳定地提高中文信息检索的检索效果。本论文中提出并实现了一种基于上下文的查询扩展方法,可以根据查询的上下文对扩展词进行选择,是一种相对“智能”的查询扩展方法。在TREC - 9 中文信息检索测试集上进行的实验表明,相对于通常简单的查询扩展,基于上下文的查询扩展方法取得了具有统计意义提高的检索效果。     

基于频繁项集和相关性的局部反馈查询扩展

针对信息检索中存在的词不匹配问题,提出一种基于频繁项集和相关性的局部反馈查询扩展算法。设计查询扩展模型和扩展词权重计算方法,从前列n篇初检文档中,挖掘同时含有查询词项、非查询词项的频繁项集,在该频繁项集中提取非查询词项作为候选扩展词,计算每个候选扩展词与整个查询的相关性,并根据该相关性得到最终的扩展词,以此实现查询扩展。实验结果表明,该算法能有效提高信息检索的性能。     

Rule-based query personalization in digital libraries

Searching a digital library is typically a tedious task. A system can improve information access by building on knowledge about a user acquired in a user profile in order to customize information access both in terms of the information returned in response to a query (query personalization) as well as in terms of the presentation of the results (presentation personalization). In this paper, we focus on query personalization in digital libraries; in particular, we address structured queries involving metadata stored in relational databases. We describe the specification of user preferences at the level of a user profile and the process of query personalization with the use of query-rewriting rules.     

Personalized query suggestion diversification in information retrieval

Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models that either focus on adapting to a specific user(personalization)or on diversifying query aspects in order to maximize the probability of the user being satisfied(diversification).We consider the task of generating query suggestions that are both personalized and diversified.We propose a personalized query suggestion diversification(PQSD)model,where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user's search context in their current session.Query aspects are identified through clicked documents based on the open directory project(ODP)with a latent dirichlet allocation(LDA)topic model.We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online(AOL)query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification.The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries,especially when more query suggestions are returned in the list.     

Evaluating the Effectiveness of Personalized Web Search

Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries.     

AN INTERACTIVE SEARCH ASSISTANT ARCHITECTURE BASED ON INTRINSIC QUERY STREAM CHARACTERISTICS

Search engine query log mining has evolved over time to more like data stream mining due to the endless and continuous sequence of queries known as query stream. In this paper, we propose an online frequent sequence discovery (OFSD) algorithm to extract frequent phrases from within query streams, based on a new frequency rate metric, which is suitable for query stream mining. OFSD is an online, single pass, and real-time frequent sequence miner appropriate for data streams. The frequent phrases extracted by the OFSD algorithm are used to guide novice Web search engine users to complete their search queries more efficiently. YourEye, our online phrase recommender is then introduced. The advantages of YourEye compared with Google Suggest, a service powered by Google for phrase suggestion, is also described. Various characteristics of two specific Web search engine query logs are analyzed and then the query logs are used to evaluate YourEye. The experimental results confirm the significant benefit of monitoring frequent phrases within the queries instead of the whole queries because none-separable items. The number of the monitored elements substantially decreases, which results in smaller memory consumption as well as better performance. Re-ranking the retrieved pages based on past users clicks for each frequent phrase extracted by OFSD is also introduced. The preliminary results show the advantages of the proposed method compared to the similar work reported in Smyth et al.     

基于用户日志的查询扩展统计模型

信息检索长期存在着用词歧义性问题,在Web搜索上的表现更加突出.提出了一种基于用户查询日志的查询扩展统计模型,将用户查询中使用的词或短语与文档中出现的相应词或短语以条件概率的形式连接,利用贝叶斯公式挑选出文档中与该查询关联最紧密的词加入原查询,以达到扩展优化的目的.实验结果表明,该方法更适宜改进Web上的信息检索,相对传统的查询扩展算法可以大幅度提高查询精度.     

LACO: A location-aware cooperative query system for securely personalized services

Location-aware computing technology becomes promising for pervasive personalization services which run anytime, anywhere, and on any device. These services should be based on contextual queries that are provided in a fast and flexible manner. To do so, cooperative query answering may be useful to support query relaxation and to provide both approximate matches as well as exact matches. To facilitate query relaxation, a knowledge representation framework has been widely adopted which accommodates semantic relationships or distance metrics to represent similarities among data values. However, research shows few legacy cooperative query mechanisms that consider location-awareness. Hence, the purpose of this paper is to propose a securely personalized location-aware cooperative query that supports conceptual distance metric among data values, while considering privacy concerns around user context awareness. To show the feasibility of the methodology proposed in this paper, we have implemented a prototype system, LACO, in the area of site search in an actual large-scale shopping mall.     

基于URL主题的查询分类方法

互联网上很多资源蕴含人类群体智慧.分类网站目录人工地对网站按照主题进行组织.基于网站目录中具有主题标注的URL设计URL主题分类器,结合伪相关反馈技术以及搜索引擎查询日志,提出了自动、快速、有效的查询主题分类方法.具体地,方法为2种策略的结合.策略1通过计算搜索结果中URL的主题分布预测查询主题,策略2基于查询日志点击关系,利用具有主题标注的URL,对查询进行标注获取数据并训练统计分类器预测查询主题.实验表明,方法可获得比当前最好算法更好的准确率,更好的在线处理效率并且可基于查询日志自动获取训练数据,具有良好的可扩展性.     

Access数据库查询教学中的知识点分析

针对非计算机专业学生在学习Access数据库查询过程中遇到的困难,采用对比的方法分析查询的基本概念,提出了选择查询、参数查询、交叉表查询、操作查询和SQL查询在教学过程中需要注意的问题,对初学者正确理解和使用查询有一定的指导意义。     

基于权重标准化SimRank方法的查询扩展技术研究

查询扩展是信息检索中的一项重要技术。传统的局部分析查询扩展方法利用伪相关文档作为候选词集合,然而部分伪相关文档并不具有很高的相关性。该文利用真实的搜索引擎查询日志,建立了查询点击图,经过多次图结构的转化得到能够反映词之间关联程度的词项关系图,并在图结构的相似度算法SimRank的基础上,提出了一种基于权重标准化的改进SimRank方法,该方法利用词项关系图中词项的全局和间接关系,能够有效挖掘与原始查询相关联的扩展词。同时,为降低SimRank算法的计算复杂度,该文采用了剪枝等策略进行优化,使得计算效率有大幅提高。在TREC标准数据集上的实验表明,该文的方法可以有效地选择相关扩展词。MAP指标较局部分析查询扩展方法提高了1.81%,在P@10和P@20指标评价中效果分别提高了5.44%和3.73%。     

智能教学系统NKI-Tutor的知识查询设计

NKI-Tutor是国家知识基础设施面向应用的智能教学系统，其功能是将NKI丰富的知识传授给用户。知识查询是NKI-Tulor给用户提供的一种查询式学习模式。该文研究如何通过NKI-Tutor的知识查询界面实现用户与智能教学系统的交互而获得所需知识，提出了基于自然语言理解的知识查询方法，用于构造和实现NKI-Tutor中用户查询知识的交互界面。该方法包括3方面内容：(1)分析用户的知识查询形式，采集用户查询句型模板；(2)根据用户查询的知识特征，把知识查询映射到概念．关系模型上；(3)通过智能分词和模糊匹配构造查询结果。