基于标签聚类的电子商务网站分类目录改善研究

本研究针对电子商务网站用户对商品概念认知与网站实际分类目录不匹配,导致检索效率低下的问题,提出了基于用户标签的电子商务网站分类目录改善方案,即将用户标签进行多层聚类,将聚类结果以层级结构的形式展示,并实现标签聚类结果和网站分类目录的映射,从而提高电子商务网站的分类检索效率和分类导航性能。     

基于模块化聚类的标签弹性推荐

[目的/意义]社会化标注系统为用户检索提供便利的同时也面临一些困扰,标签推荐研究有助于解决资源检索中精确度与召回率之间的两难抉择。[过程/方法]借助网络科学的理论与方法,通过对标签网络的模块化聚类处理获得主题聚类,采用度数中心度对主题聚类内部标签进行排名,根据特定规则选取Top-N标签数量。[结果/结论]实验结果显示,研究中提出的模块化Top-N标签推荐方法,具有可逐层细化的精确度和良好的召回率。该方法的弹性机制可为不同的检索要求提供差异化服务。     

基于贝叶斯层级模型的社会化标注主题聚类探讨

在资源共享性的社会化标注网站上,大量无标签或者缺少标签的资源往往会因为标签信息的不完整,以致无法被有效地利用和检索。为了有效地进行资源检索,借助于贝叶斯层级模型,对被标注信息资源的主题进行聚类,并生成最终的主题聚类结果,相关实验结果显示了其有效性。     

大众分类中标签检索的局限与优化研究

进入Web2.0时代,大众分类成为其中一种主要的信息组织方式之一,而大众分类则是通过标签系统实现对网络信息资源的分类,因此标签在大众分类中占据重要地位。以豆瓣网中的"豆瓣电影"为例,分析标签检索存在的局限,并提出对应的优化方法和措施。     

元数据描述对搜索引擎排序结果影响研究

基于元数据描述在搜索引擎排序算法中权重及对排序结果的影响越来越小这一问题,本文首先分析了元数据标签的存在价值及其对检索结果优化本应起到的作用,并对如何优化元数据标签,使其更好的揭示网页内容与特定主题的相关度这一问题进行了讨论。其次,在分析的基础上,通过实证研究的方法,利用不同主题范围的检索词在搜索引擎检索的结果,对元数据在搜索引擎当中的实际使用和优化情况进行了统计调查和建模分析,借此,考察元数据描述对搜索引擎排序结果的实际影响和意义,并提出了未来元数据描述在网页编写及搜索结果排序中应注意的问题。     

模糊c均值聚类在wav格式音频检索中的研究

应用模糊c均值聚类方法对wav格式的音频检索。不同于以前为音频建模和分类的监督机制,而是通过对音频数据的特征进行模糊聚类形成聚类质心,根据聚类质心的相似度匹配来实现非监督机制的音频检索,并优化参数以提高检索准确度。     

基于标签聚类与用户模型的个性化推荐方法研究

社会标签系统是Web2.0中提出的概念,旨在更好地表达用户的兴趣和意愿。而标签聚类是社会标签系统的个性化推荐中一个重要的研究课题。本文研究了如何基于标签聚类与用户模型来进行个性化推荐的方法。通过计算标签的相似度进行标签聚类,结合用户模型,根据标签聚类结果做出推荐。通过采用CiteULike公布的数据集进行实验证明,与未采用标签聚类的推荐方法相比,本方法不仅可提高推荐的命中率,优化目标资源的排名,而且能为用户发现更多新的感兴趣的资源。     

基于游记主题挖掘与表达的旅游信息推荐研究

[目的/意义]针对在线旅游平台,提出一种挖掘游记主题标签,以代表性游记以及其中相关内容进行旅游信息推荐的新策略。[方法/过程]在利用文本挖掘技术,构建LDA主题模型,形成游记文本主题标签的基础上,通过游记代表度算法,筛选出针对相应标签的高描述度与高忠诚度游记进行旅游信息推荐,以客观表达文本聚类结果以及主题词之间的语义关系,并以蚂蜂窝旅游网中的"杭州游记"为例,加以验证。[结果/结论]结果表明,这种方式能挖掘出旅游者在历史旅游经历中真实的旅游热点及重点信息需求,针对高相似度游记的识别与聚类具有良好效果,对旅游信息细粒度推荐具有指导意义与实践意义。     

基于在线问诊文本信息的医生标签自动生成研究

【目的/意义】为在线医疗问诊平台中的医生自动生成高质量标签,更好地服务于对医生资源的分类、检索和管理。【方法/过程】基于在线问诊文本信息,提出了结合时间周期特征与文本主题特征的医生标签自动生成算法。首先根据医生相关文本信息提取关键词生成候选标签,然后从患者问题文本和医生回答文本两个方面进行LDA主题模型训练,按时间周期挖掘出问题文本和回答文本的主题特征,对候选标签进行质量控制;最后经标签加权混合后得到最终的医生标签。【结果/结论】实验结果表明,该标签自动生成算法能够反映出医生标签生成的动态性,能够准确生成符合医生专业知识特征的高质量标签,具有较好的标签生成效果。     

分众分类与书目记录结合研究

选取国内外分众分类与书目记录结合的22个网站进行了调查分析,旨在通过对应用分众分类的图书馆网站及针对书目等学术类资源的社会标签系统这两类网站的调查研究,明确当前两类系统结合的功能体现及特点,在此基础上提出分众分类应用于图书馆的未来发展建议,以最终实现对书目信息和社会书签系统资源的有效管理和利用,推动图书馆服务的更好发展。     

基于用户兴趣的个性化信息检索方法研究

在信息检索中对基于用户兴趣的检索结果进行重排得到广泛关注。为了构建用户兴趣的知识库,本文对用户的登录细节和点击数据进行综合分析,提出了定制用户访问信息的方法,同时采用开放式目录项目Dmoz自动进行用户兴趣主题映射,对搜索结果进行个性化分类,并根据用户兴趣对检索结果重排,比正常的搜索引擎更容易找到相关的信息。联机实验结果表明,本文提出的方法可有效地提高用户检索精度。     

搜索引擎搜索结果的评价技术

搜索引擎根据用户查询在自己的索引数据库中进行查找，并根据相关性分析将查询结果返回给用户。本文就传统信息检索系统的性能效率评价技术,针对Internet的特点，对搜索引擎搜索结果的评价进行了讨论。     

Topic distillation via sub-site retrieval

Topic distillation is one of the main information needs when users search the Web. Previous approaches for topic distillation treat single page as the basic searching unit, which has not fully utilized the structure information of the Web. In this paper, we propose a novel concept for topic distillation, named sub-site retrieval, in which the basic searching unit is sub-site instead of single page. A sub-site is the subset of a website, consisting of a structural collection of pages. The key of sub-site retrieval includes (1) extracting effective features for the representation of a sub-site using both the content and structure information, (2) delivering the sub-site-based retrieval results with a friendly and informative user interface. For the first point, we propose Punished Integration algorithm, which is based on the modeling of the growth of websites. For the second point, we design a user interface to better illustrate the search results of sub-site retrieval. Testing on the topic distillation task of TREC 2003 and 2004, sub-site retrieval leads to significant improvement of retrieval performance over the previous methods based on single pages. Furthermore, time complexity analysis shows that sub-site retrieval can be integrated into the index component of search engines.     

Toward a reconceptualization of information seeking research: focus on the exchange of meaning

This paper reports the results of a preliminary study of interpersonal information seeking interactions between a user and a human information source. The study showed that users specify their information needs (uncertainty) largely in terms of what they know (certainty) during the interaction. The articulated certainty and uncertainty in the interaction can be classified as utterances focusing on either the topic (what the user is talking about) or comment (how that topic fits in with the user's situation or problem). We suggest that user studies in information seeking research should conceptually realign from an emphasis on user's uncertainty- and topic-based matching to the inclusion of user's certainty and comment dimensions in order to develop a more linguistically robust, multi-dimensional approach to matching for information retrieval.     

用户认知观视野中的情报检索

回溯了情报检索的历程及研究的重点线路，分析了传统的情报检索及情报检索系统存在的一些难题。针对这些问题，指出了应从用户认知观角度来探索情报检索研究，说明了用户认知观的研究对情报检索理论带来的影响，并从用户认知观的角度对情报检索系统的开发提出了一些设想。     

医疗类社会化搜索答案质量用户满意度模型及实证研究

社会化搜索是一种新兴的、基于Web2.0框架的搜索模式,在此模式基础上的医疗类社会化搜索平台目前已成为缓解医疗资源紧张的重要手段之一。本文基于健康信念模型和顾客满意度模型,以用户为中心,结合当前的时代背景,构建医疗类社会化搜索答案质量用户满意度模型,并通过问卷调研修正模型。研究发现：医疗类社会化搜索平台答案的来源质量对健康行为期望和健康威胁感知均有显著影响,是最具影响力的一个质量维度;情感质量对用户的自我效能影响显著;健康行为期望和健康威胁感知对感知价值的影响高于对满意度的影响;而感知价值和自我效能是影响用户满意度的关键指标。     

Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web

Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.     

The effectiveness of query-specific hierarchic clustering in information retrieval

Hierarchic document clustering has been widely applied to information retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search (IFS). However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional IFS. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR.     

Evaluating subtopic retrieval methods: Clustering versus diversification of search results

To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.