基于文档属性单元松弛的XML近似查询方法

为解决普通用户对XML文档的近似查询问题,提出了一种基于文档属性单元松弛的XML近似查询方法.该方法将XML文档中的叶子结点和属性结点作为属性单元处理,基于一致集的概念导出最大集,生成最小非平凡函数依赖集,从而找出属性单元之间的近似函数依赖关系,进而求出近似候选码和近似关键字.在此基础上,根据属性单元支持度将属性单元按重要程度排列并据此对初始查询条件进行松弛,最不重要的属性单元最先松弛并且松弛程度最大.利用松弛后的查询条件对XML文档进行查询,可得到与初始查询条件近似的查询结果.实验结果和分析表明:提出的XML近似查询方法能够很好地满足用户的查询意图,具有较高的执行效率.     

一种基于领域知识的XML数据模糊查询

为了解决普通用户对XML数据的模糊查询问题,提出了一种基于领域知识的XML数据模糊查询方法.以模糊集理论为基础,首先介绍了XML数据模糊查询的构成形式;然后提出了将领域知识和模糊集的隶属函数相结合的方法实现XML数据的模糊查询条件转换,转换过程考虑了查询谓词的重要程度和用户偏好;最后按结果元素对模糊查询的满足程度对模糊查询结果进行排序.该方法无需改变传统的XML查询语言和XDBMS就能够实现模糊查询,从而提高了用户与系统之间的交互能力.实验结果表明,提出的模糊查询方法具有较高的查全率和准确率.     

基于查询片段松弛的XML小枝近似查询方法

为了解决普通用户对XML文档的近似查询问题,提出一种基于查询片段松弛的XML小枝近似查询方法.该方法利用查询历史推测用户偏好,进而根据用户偏好为原始小枝查询中的每个查询片段分配重要程度,然后基于查询片段重要程度对原始小枝查询条件进行松弛处理,最不重要的查询片段最先松弛,从而确保获取最为相关的查询结果;最后,根据对原始查询和用户偏好的满足程度,将得到的满足松弛查询条件的结果进行排序.实验结果表明,本文提出的查询松弛和结果排序方法能够获得较高的查全率和准确率,并且能较好地满足用户需求和偏好.     

基于感知哈希与用户偏好的检索意图建模方法

针对商品检索排序问题,提出结合用户查询条件与用户浏览兴趣偏好的排序方法,目的是在不增加用户输入查询条件的前提下,提高用户对商品检索结果的满意度。根据用户提交的查询条件,对数据库中的商品进行筛选和初步排序。在此基础上,以用户的浏览行为分析用户对商品的兴趣浓度,并从用户的历史浏览记录中提取出用户的兴趣偏好模型,计算商品属性信息与用户偏好模型之间的相似度大小,对返回的排序结果进行调整优化。实验表明,基于用户兴趣偏好的排序结果更加符合用户的检索意图。     

一种基于XML小枝查询片段松弛的近似查询与结果排序方法

提出了一种基于XML小枝查询片段松弛的近似查询与结果排序方法来实现用户在XML文档中的近似查询:通过收集用户的查询历史来推测用户偏好,并以此计算原始小枝查询分解得到的查询片段的重要程度,然后按照重要程度的排序进行查询松弛;在松弛方法中,根据查询片段数目的不同采用不同的松弛方法,若片段数目较多则以查询片段为粒度对其松弛,较少则以查询结点为粒度对数值查询与非数值查询采用不同的方法进行松弛,得到最为相关的近似查询结果;最后按近似查询结果对原始查询和用户偏好的满足程度进行排序并输出。实验证明,该近似查询方法能够较好地满足用户的需求和偏好,具有较高的查全率和准确率。     

基于粗糙集的个性化Web搜索系统

本文提出了一种基于粗糙集理论的个性化web搜索系统。用户偏好文件中对关键字进行分组以表示用户兴趣类别。利用粗糙集理论处理自然语言的内在含糊性，根据用户偏好文件对查询条件进行扩展。搜索组件使用扩展后的查询条件搜索相关信息。为了进一步排除不相关信息，排序组件计算查询条件和搜索结果之间的相似程度，根据计算值对搜索结果进行排序。与传统搜索引擎进行了比较，实验结果表明，该系统有效地提高了搜索结果的精度，满足了用户的个性化需求。     

基于地理时间意图和偏好的个性化Web搜索框架GT WSearch

基于Web查询的地理位置、时间查询意图和用户偏好的个性化Web搜索可以改善Web搜索结果,更好地满足不同用户的信息需求。提出了GT-WSearch个性化Web搜索框架,它通过挖掘搜索结果、用户点击数据和对查询进行分析得到的用户概貌和查询概貌,来捕捉用户的地理-时间的意图和偏好,提高搜索质量。用户概貌表明了查询自身的地理-时间的特性。 GT-WSearch框架在排序函数中利用文档的地理位置、时间的相关度来进行个性化搜索。 最后将使用线性的相关度排序函数进行重新排序的搜索结果返回给用户。大量实验结果表明,所提出的个性化方法在提高Web搜索结果的质量中取得了明显的效果。     

基于关键字之间结构关系的XML查询结果排序方法

非空结果的XML关键字查询中,多个查询关键字之间必然存在联系,这种联系可以通过SLCA(最紧致片段)的结构关系获得.基于SLCA的结构关系,提出了一种推测多个关键字内在联系的XML关键字查询结果排序方法:通过LISA Ⅱ 算法获得SLCA;根据SLCA的结构信息推测出各个关键字之间的内在结构关系,得到所有关键字组成的关系树;然后根据关系树中各关键字对查询结点的严格程度得到对应SLCA的重要程度,据此得到有序的SLCA并输出.该方法利用了XML文档的结构信息对查询结果进行排序.实验结果和分析表明,提出的方法具有较高的准确率,能够较好地满足当前用户的需求和偏好.     

XML数据查询中值匹配查询代价估计算法

XML数据查询中值匹配查询条件的查询代价估计问题是一种典型的多元素查询条件代价估计问题.它与传统关系型数据库中的多元素查询条件不同,因为XML数据中的值信息分布不仅与其他值信息分布相关,还与XML数据中的结构信息相关,而且当XML数据结构比较复杂时,可能会形成高维元素相关.针对以上问题,提出了一种面向XML数据的基于小波的多维直方图查询代价估计算法,并提出了确定XML数据中以某值元素为主键的相互依赖元组的方法,将值匹配条件改写为多元素查询条件的方法以及结构信息的值化方法.实验结果证明,提出的方法取得了较准确的查询代价估计结果.     

基于关键词权重的XML查询结果排序方法

XML关键字查询结果质量不高的一个很重要的原因是查询关键词难以反映用户真实的查询意图,而给关键词设置权重在一定程度上可以解决这一难题. 本文结合关键字之间的结构关系提出了一种新的结果排序方法,该方法给查询关键词设置权重,并参照查询关键词的权重给包含关键字的结点设定结点权重,然后根据关系树中的结点权重和关键词之间结构关系^[1]统计SLCA结点的重要程度,再以此依据对查询结果进行排序,最后返回给用户有序的查询结果. 实验结果和分析表明,提出的排序方法具有较高的准确率,能够较好地满足用户查询的需求和偏好.     

Adaptive query relaxation and top-<Emphasis Type="Italic">k</Emphasis> result ranking over autonomous web databases

Internet users may suffer the empty or too little answer problem when they post a strict query to the Web database. To address this problem, we develop a general framework to enable automatically query relaxation and top-k result ranking. Our framework consists of two processing steps. The first step is query relaxation. Based on the user original query, we speculate how much the user cares about each specified attribute by measuring its specified value distribution in the database. The rare distribution of the specified value of the attribute indicates the attribute may important for the user. According to the attribute importance, the original query is then rewritten as a relaxed query by expanding each query criterion range. The relaxed degree on each specified attribute is varied with the attribute weight adaptively. The most important attribute is relaxed with the minimum degree so that the answer returned by the relaxed query can be most relevant to the user original intention. The second step is top-k result ranking. In this step, we first generate user contextual preferences from query history and then use them to create a priori orders of tuples during the off-line pre-processing. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at querying time to expeditiously provide top-k relevant answers by using the top-k evaluation algorithm. Results of a preliminary user study demonstrate our query relaxation, and top-k result ranking methods can capture the users preferences effectively. The efficiency and effectiveness of our approach is also demonstrated.     

Personalized ranking in web databases: establishing and utilizing an appropriate workload

The emergence of the deep Web has given a new connotation to the concept of ranking database query results. Earlier approaches for ranking either resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for holistically supporting user- and query-dependent ranking has been recently proposed (Telang et al. in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011). An important component of this framework is a workload consisting of ranking functions, wherein each function represents an individual user’s preferences towards the results of a specific query. At the time of answering a query for which no prior ranking function exists, the similarity model is employed, and is expected to ensure a good quality of ranking as long as a ranking function for a very similar user-query pair exists in this workload. In this paper, we address the problem of determining an appropriate set of user-query pairs to form a workload of ranking functions to support user- and query-dependent ranking for Web databases. We propose a novel metric, termed workload goodness, that quantifies the notion of a “good” workload into an absolute value. The process of finding such a workload of optimal goodness is a combinatorially explosive problem; therefore, we propose a heuristic solution, and advance three approaches for determining an acceptable workload, in a static as well as a dynamic environment. We discuss the effectiveness of our proposal analytically as well as experimentally over two Web databases.     

Top-k集合空间关键字近似查询方法

近年来,带有位置和文本信息的空间-文本数据的规模迅速增长,以空间-文本数据为背景的空间关键字查询技术得到广泛的研究与应用。现有大多数空间关键字查询方法通常以单个空间对象作为查询结果的基本单元,最近有少数研究工作提出以一组空间对象作为查询结果的基本单元,这组空间对象联合满足用户的查询需求,但却没有考虑组内空间对象之间的关联关系。针对上述问题,提出一种top-[k]集合空间关键字近似查询方法。提出一种基于关联规则的空间对象之间的关联访问度评估方法,设计了一种结合距离和组内空间对象关联访问度的评分函数;提出了一种基于VP-Tree的剪枝策略,用于快速搜索空间对象的局部邻域,进而加快查询匹配速度;利用评分函数计算候选空间对象组合的得分,并以此选取top-[k]组空间对象作为查询结果。实验结果表明,提出的空间对象关联度评估方法具有较高的准确性,提出的剪枝策略具有较高的执行效率,获取的top-[k]组空间对象具有较高的用户满意度。     

Selective sampling techniques for feedback-based data retrieval

As many databases have been brought online, data retrieval??finding relevant data from large databases??has become a nontrivial task. A feedback-based data retrieval system was proposed to provide user with an intuitive way for expressing their preferences in queries. The system iteratively receives a partial ordering on a sample of data from the user, learns a ranking function, and returns highly ranked results according to the function. An important issue in such retrieval systems is minimizing the number of iterations or the amount of feedback to learn an accurate ranking function. This paper proposes selective sampling (or active learning) techniques for RankSVM that can be used in the retrieval systems. The proposed techniques minimizes the amount of user interaction to learn an accurate ranking function thus facilitates users formulating a preference query in the data retrieval system.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

Group decision making with incomplete information: a dominance and quasi‐optimality volume‐based approach using Monte‐Carlo simulation

In this paper, we present a comprehensive framework for multiattribute group decision making considering that neither information about individual preferences nor the importance of individual decision makers for the group is available in exact form. We study several different forms of incomplete preference information, including a ranking of attribute weights, ranking of values of alternatives in each attribute, and ranking of value differences. Our approach is based on relative volumes in parameter space and allows for probabilistic statements about different results, including optimality or quasi‐optimality of alternatives, or relations between alternatives.     

An adaptive learning automata-based ranking function discovery algorithm

Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the same query by different users for different aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. In this paper, a learning automata-based ranking function discovery algorithm in which different sources of information are combined is proposed. In this method, the learning automaton is used to adjust the portion of the final ranking that is assigned to each source of evidence based on the user feedback. All sources of information are first given the same importance. The proportion of a given source increases, if the documents provided by this source are reviewed by the user and decreases otherwise. As the proposed algorithm proceeds, the probability of appearance of each source in the final ranking gets proportional to its relevance to the user queries. Several simulation experiments are conducted on well-known data collections and query types to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing methods in terms of precision at position n, mean average precision, and normalized discount cumulative gain.     

Semantic relation based personalized ranking approach for engineering document retrieval

Since engineering design is heavily informational, engineers want to retrieve existing engineering documents accurately during the product development process. However, engineers have difficulties searching for documents because of low retrieval accuracy. One of the reasons for this is the limitation of existing document ranking approaches, in which relationships between terms in documents are not considered to assess the relevance of the retrieved documents. Therefore, we propose a new ranking approach that provides more correct evaluation of document relevance to a given query. Our approach exploits domain ontology to consider relationships among terms in the relevance scoring process. Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score. In our ranking approach, user interests and searching intent are also considered in order to provide personalized services. The experimental results show that the proposed approach outperforms existing ranking approaches. A precisely represented semantics of a document as a graph and multiple relation-based weighting schemes are important factors underlying the notable improvement.     

A Cascade-Hybrid Music Recommender System for mobile services based on musical genre classification and personality diagnosis

In this paper, we present a Cascade-Hybrid Music Recommender System intended to operate as a mobile service. Specifically, our system is a middleware that realizes the recommendation process based on a combination of music genre classification and personality diagnosis. A mobile user is able to query for music files by simply sending an example music file from his/her mobile device. In response to the user query, the system recommends music files that not only belong to the same genre as the user query, but also an attempt has been made to take into account both the user preferences as well as ratings from other users for candidate results. The recommendation mechanism is realized by applying the collaborative filtering technique of personality diagnosis. Using the minimum absolute error and the ranked scoring criteria, our approach is compared to existing recommendation techniques that rely on either collaborative filtering or content-based approaches. The outcome of the comparison clearly indicates that our approach exhibits significantly higher performance.     

基于短期记忆与遗忘系数的用户个性化建模方法研究

搜索引擎的一个标准是不同的用户用相同的查询条件检索时,返回的结果相同。为解决准确性问题,个性化搜索引擎被提出,它可以根据用户的不同个性化特征提供不同的搜索结果。然而,现有的方法更注重用户的长时记忆和独立的用户日志文件,从而降低了个性化搜索的有效性。获取用户短时记忆模型来提供准确有效的用户偏好的个性化搜索方法被广泛采用。首先,根据基于查询关键词的相关概念生成短期记忆模型;接着,基于用户的时序有效点击数据生成用户个性化模型;最后,在用户会话中引入了遗忘因子来优化用户个性化模型。实验结果表明,所提出的方法可以较好地表达用户信息需求,较为准确地构建用户的个性化模型。