用户驱动的微博可视化搜索

目的 微博作为一个社交与信息分享平台,日信息量数以亿计,如何高效地搜索用户感兴趣的信息成为亟待解决的问题.提出了一个新颖的用户驱动的可视化微博信息搜索方法.方法 采用特征词及其权重来建模用户的兴趣特征,并基于此建立用户与特征词之间的相关关系.搜索微博信息时,首先定位与检索词相关的微博用户,在相关微博用户的微博中筛选与搜索相关的微博.另外,采用关注度传递算法对搜索进行扩展,将返回的特征词和微博用户进行可视化展示,并提供交互供用户查看与选定特征词或用户相关的微博.结果 实验结果表明,基于本文方法,用户可以高效地定位感兴趣的微博信息.结论 以用户作为桥梁,大大缩小了微博信息的搜索范围,同时采用关注度传递算法对搜索进行扩展,对结果进行可视化展示.实验表明本文方法能够使用户快速搜索出感兴趣的信息.     

基于吸收态随机行走的两阶段效用性查询推荐方法

搜索引擎已经成为人们获取信息的重要途径,然而对于用户而言如何构造一个合适的查询仍然是一项困难的工作.为了减轻用户搜索信息的负担,查询推荐技术应运而生并且已经成为当今搜索引擎不可或缺的组成部分.传统的查询推荐方法主要关注向用户推荐相关性查询,即推荐与源查询具有相近搜索意图的其他查询.然而查询推荐的根本目标是帮助用户成功完成其搜索任务,而不仅仅是找到相关性查询,尽管相关性查询有时也能得到有用的搜索结果.为了更好地满足用户的搜索目标,一种更直接的查询推荐方式是向用户推荐高效用性查询,即能够更好满足用户信息需求的查询.提出了一个基于吸收态随机行走的2阶段效用性查询推荐方法,该方法能够同时对用户的查询重构行为和查询点击行为进行建模并推导出查询的效用.在真实查询日志上的实验结果表明:新方法在评价指标查询相关率(query relevant ratio, QRR)和平均相关文档数(mean relevant document, MRD)上要显著优于其他5种基准方法.     

元搜索引擎的现状与发展

元搜索引擎利用现有的独立搜索引擎的查询性能,将搜索引擎看成一个整体,为用户提供一个统一的查询界面与返回结果。介绍了目前网络上比较著名和流行的一些元搜索引擎,对近几年来关于元搜索引擎的研究进行了分析总结,旨在为对元搜索引擎的进一步研究提供参考。     

Influences of general computer experience and age on library database search performance

Two experiments examined the effects of general computer experience and age on library system search performance among novice library system users. Twenty younger adults (10 with high and 10 with low computer experience) and 20 older adults (10 with low and 10 with no computer experience) performed 10 search tasks of varying difficulty. Search success, syntax errors, database field specifications, keyword specifications, and use of Boolean operators were examined. Among younger novices, high computer experience was associated with slightly better performance than low computer experience. Among older novices, having some computer experience was associated with much better performance than no computer experience. Older computer users showed lower overall success rates, made more syntax and field specification errors, and demonstrated poorer understanding of Boolean logic and keyword matching algorithms than younger adults with similar computer experience. Implications for interface design and training interventions for novice on-line library system users are discussed.     

基于知识的网页检索工具

随着因特网在全球范围的广泛使用,越来越多的人们借助于因特网从事科研和商务活动,而网页检索工具成了人们必不可少的软件工具．然而,目前流行的检索工具大多基于关键字查询,常常出现信息过载或有用信息丢失等现象．造成这一原因主要有两方面：用户提交的查询不能很好地表达他的目的;查询的结果没有建立有效的索引机制,引导人们快速找到有用信息。为此我们提出一种基于知识的网页检索工具（ＫＷＳＥ）,它是在已有的检索工具的     

Assisting web search using query suggestion based on word similarity measure and query modification patterns

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulating keyword queries that improve the precision and recall of search results. WebQS relies on frequency of occurrence, keyword similarity measures, and modification patterns of queries in user query logs, which capture information on millions of searches conducted by millions of users, to suggest useful queries/query keywords during the user query construction process and achieve the design goal of QS. Experimental results show that WebQS performs as well as Yahoo! and Bing in terms of effectiveness and efficiency and is comparable to Google in terms of query suggestion time.     

个性化搜索引擎系统机制的研究

随着网络信息资源的迅速增加,个性化信息服务越来越成为信息检索领域中研究的热点,针对传统搜索引擎系统的缺点,提出了一种新型个性化搜索引擎系统的体系结构,并在此基础上给出了系统中个性化机制的相关算法,同时使用基于关键词的搜索,利用Web挖掘技术,在实现为不同用户提供不同检索结果的同时提高了个性化查询的精确度和速度,保证了全查率.     

基于用户查询日志的网络搜索主题分析

网络搜索分析在优化搜索引擎方面具有举足轻重的作用,而且对用户个人搜索特性进行分析能够提高搜索引擎的精准度。目前,大多数已有模型（比如点击图模型及其变体）,注重研究用户群体的共同特点。然而,关于如何做到既可以获取用户群体共同特点又可以获取用户个人特点方面的研究却非常少。本文研究了基于个人用户网络搜索分析新问题,即通过研究用户搜索的突发性现象,获取个人用户搜索查询的主题分布情况。提出了两个搜索主题模型,即搜索突发性模型（SBM）和耦合敏感搜索突发性模型（CS-SBM）。SBM假设查询词和URL主题是无关的,CS-SBM假设查询词和URL之间是有主题关联的,得到的主题分布信息存储在偏Dirichlet先验中,采用Beta分布刻画用户搜索的时间特性。实验结果表明,每一个用户的网络搜索轨迹都有多种基于用户的独有特点。同时,在使用大量真实用户查询日志数据情况下,与LDA、DCMLDA、TOT相比,本文提出的模型具有明显的泛化性能优势,并且有效地描绘了用户搜索查询主题在时间上的变化过程。     

A Knowledge-Based Approach to Effective Document Retrieval

This paper presents a knowledge-based approach to effective document retrieval. This approach is based on a dual document model that consists of a document type hierarchy and a folder organization. A predicate-based document query language is proposed to enable users to precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. A guided search tool is developed as an intelligent natural language oriented user interface to assist users formulating queries. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users' particular interests. A knowledge-based query processing and search engine is devised as the core component in this approach. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query.     

基于移动互联网日志的搜索引擎用户行为研究

随着移动互联网的迅速发展,移动搜索用户大规模增加,移动搜索引擎用户行为分析对改进搜索引擎性能,提高用户体验具有重要意义。该文选取某移动搜索引擎2011年6月第一周的日志,对移动互联网用户搜索行为进行分析和研究。我们从查询词分析、会话分析以及用户点击分析3个角度出发,对查询词长度和频度、问题式查询和网址查询比例、会话内查询个数、查询词修改方式以及用户点击位置进行研究,并与互联网搜索引擎相应指标进行对比。相关分析结论对于移动搜索引擎算法改进与系统优化具有一定参考意义。     

Implementing and evaluating phrasal query suggestions for proximity search

This paper describes and evaluates a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator near being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine specializes the query through the use of super phrases; if the list is too short, the engine generalizes the query through the use of proximal subphrases.We describe methods for generating both types of suggestions and present algorithms for ranking the suggestions. Specifically, we present the problem of counting proximal subphrases for specialization and the problem of counting unordered super phrases for generalization.The uptake of our approach was evaluated by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. We looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries. Efficacy was evaluated through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrasal query suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an Okapi BM25 ranking algorithm.     

Efficient fuzzy full-text type-ahead search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step toward solving this problem. In this paper, we study a new information-access paradigm, called “type-ahead search” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by allowing keywords to appear at different places in the underlying data. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms for both single-keyword queries and multi-keyword queries, using previously computed and cached results in order to achieve a high interactive speed. We develop novel techniques to support fuzzy search by allowing mismatches between query keywords and answers. We have deployed several real prototypes using these techniques. One of them has been deployed to support type-ahead search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.     

元数据驱动的个性化查询工具设计与实现

传统查询定制工具只关注动态组合SQL语句,并没有关注与业务相关的实体,如用户、专业等。用户无法定制个性化的查询,企业不能对数据按专业、查询对象和用户等组织多维度、多专业的数据查询。为解决上述问题,提出了一个元数据驱动的个性化查询定制框架,用元数据描述用户需求和企业环境。用户通过个性定制工具,形成用户需求的元数据描述,查询引擎通过元数据读取用户需求,然后查询专业数据库并形成个性化界面。既有通用查询的通用数据接口,又有友好、个性化的用户接口,在油田企业信息集成中得到应用,并取得良好应用效果。     

An efficient approach to suggesting topically related web queries using hidden topic model

Keyword-based Web search is a widely used approach for locating information on the Web. However, Web users usually suffer from the difficulties of organizing and formulating appropriate input queries due to the lack of sufficient domain knowledge, which greatly affects the search performance. An effective tool to meet the information needs of a search engine user is to suggest Web queries that are topically related to their initial inquiry. Accurately computing query-to-query similarity scores is a key to improve the quality of these suggestions. Because of the short lengths of queries, traditional pseudo-relevance or implicit-relevance based approaches expand the expression of the queries for the similarity computation. They explicitly use a search engine as a complementary source and directly extract additional features (such as terms or URLs) from the top-listed or clicked search results. In this paper, we propose a novel approach by utilizing the hidden topic as an expandable feature. This has two steps. In the offline model-learning step, a hidden topic model is trained, and for each candidate query, its posterior distribution over the hidden topic space is determined to re-express the query instead of the lexical expression. In the online query suggestion step, after inferring the topic distribution for an input query in a similar way, we then calculate the similarity between candidate queries and the input query in terms of their corresponding topic distributions; and produce a suggestion list of candidate queries based on the similarity scores. Our experimental results on two real data sets show that the hidden topic based suggestion is much more efficient than the traditional term or URL based approach, and is effective in finding topically related queries for suggestion.     

多搜索引擎权重计算及搜索结果排序质量评估

搜索引擎在多成员搜索引擎搜索结果的整合过程中,搜索结果的排序在很大程度上决定着元搜索引擎的服务质量。为了实现搜索结果的有效整合,目前技术主要结合查询请求、文档内容、初始排序或（和）赋予搜索成员搜索引擎权重等因素。其中采用赋予搜索引擎权重时,往往根据用户和技术人员经验,主观地进行赋值,不能体现真实的用户搜索偏好。为此,提出了通过挖掘用户搜索及遍历情况,动态地赋予各成员搜索引擎权重的方法。通过用户遍历及点击下载情况,得到了用户搜索遍历与返回结果的匹配度,论证了该方法的可行性和有效性。     

Rational behavior in peer-to-peer profile obfuscation for anonymous keyword search

User profiling in web search has the advantage of enabling personalized web search: the quality of the results offered by the search engine to the user is increased by taking the user’s interests into account when presenting those results. The negative side is that the interests and the query history of users may contain information considered as private; hence, technology should be provided for users to avoid profiling if they wish so. There are several anti-profiling approaches in web search, from basic level countermeasures to private information retrieval and including profile obfuscation. Except private information retrieval (PIR), which hides the retrieved item from the database, the rest of approaches focus on anonymizing the user’s identity and fall into the category of anonymous keyword search (also named sometimes user-private information retrieval). Most current PIR protocols are ill-suited to provide PIR from a search engine or large database, due to their complexity and their assumption that the database actively cooperates in the PIR protocol. Peer-to-peer profile obfuscation protocols appear as a competitive option provided that peers are rationally interested in helping each other. We present a game-theoretic analysis of P2P profile obfuscation protocols which shows under which conditions helping each other is in the peers’ rational interest.     

A statistical method for estimating the usefulness of text databases

Searching desired data on the Internet is one of the most common ways the Internet is used. No single search engine is capable of searching all data on the Internet. The approach that provides an interface for invoking multiple search engines for each user query has the potential to satisfy more users. When the number of search engines under the interface is large, invoking all search engines for each query is often not cost effective because it creates unnecessary network traffic by sending the query to a large number of useless search engines and searching these useless search engines wastes local resources. The problem can be overcome if the usefulness of every search engine with respect to each query can be predicted. We present a statistical method to estimate the usefulness of a search engine for any given query. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that our estimation method is much more accurate than existing methods.     

An exploration of risk information search via a search engine: Queries and clicks in healthcare and information security

The general public is increasingly using search engines to seek information on risks and threats. Based on a search log from a large search engine, spanning three months, this study explores user patterns of query submission and subsequent clicks in sessions, for two important risk related topics, healthcare and information security, and compares them to other randomly sampled sessions. We investigate two session-level metrics reflecting users' interactivity with a search engine: session length and query click rate. Drawing from information foraging theory, we find that session length can be characterized well by the Inverse Gaussian distribution. Among three types of sessions on different topics (healthcare, information security, and other randomly sampled sessions), we find that healthcare sessions have the most queries and the highest query click rate, and information security sessions have the lowest query click rate. In addition, sessions initiated by the users with greater search engine activity level tend to have more queries and higher query click rates. Among three types of sessions, search engine activity level shows the strongest effect on query click rate for information security sessions and weakest for healthcare sessions. We discuss theoretical and practical implications of the study.     

Analyzing and mining a code search engine usage log

This paper presents an analysis of a year long usage log of Koders, the first commercially available Internet-Scale code search engine (http://www.koders.com). The usage log comprises about ten million activities from more than three million users. Analysis of the usage data shows that despite of attracting a large number of visitors, Koders has a very sparse usage and that it lacks regular usage from many of its users. When compared to Web search, search behavior in Koders showed many similar patterns. A topic modeling analysis of the usage data shows what topics users of Koders are looking for. Observations on the prevalence of these topics among the users, and observations on how search and download activities vary across topics, lead to the conclusion that users who find code search engines usable are those who already know to a high level of specificity what to look for. This paper also presents a general categorization of these topics that provides insights on the different ways code search engine users express their queries. It identifies various forms of queries in Koders’s log and the kinds of results addressed by the queries. It also provides several suggestions for improvements in code search engines based on the analysis of usage, topics, and query forms. The work presented in this paper is the first of its kind that reveals several insights on the usage of an Internet-Scale code search engine.