基于改进LDA-FCM的UserCF知识推荐研究

针对传统知识推荐算法存在的语义缺失和精准性低问题,提出一种基于改进LDA-FCM的知识推荐算法。首先获取用户知识文档,采用主题优化的LDA模型挖掘用户知识主题。继而通过FCM算法将用户聚类,缩小相似度计算的遍历范围,并采用JS散度代替欧氏距离,实现FCM对象到用户的转换。最后基于UserCF算法构建用户对知识的兴趣指数并进行TOP-N推荐。爬取中国知网500篇期刊论文实测发现,与传统UserCF算法相比,改进算法的准确率、召回率和F1值分别提高了22.35%、55.92%、49.06%。     

Mining user interests over active topics on social networks

Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.     

Multi-Aspect Review-Team Assignment using Latent Research Areas

Reviewer assignment is an important task in many research-related activities, such as conference organization and grant-proposal adjudication. The goal is to assign each submitted artifact to a set of reviewers who can thoroughly evaluate all aspects of the artifact’s content, while, at the same time, balancing the workload of the reviewers. In this paper, we focus on textual artifacts such as conference papers, where both (aspects of) the submitted papers and (expertise areas of) the reviewers can be described with terms and/or topics extracted from the text. We propose a method for automatically assigning a team of reviewers to each submitted paper, based on the clusters of the reviewers’ publications as latent research areas. Our method extends the definition of the relevance score between reviewers and papers using the latent research areas information to find a team of reviewers for each paper, such that each individual reviewer and the team as a whole cover as many paper aspects as possible. To solve the constrained problem where each reviewer has a limited reviewing capacity, we utilize a greedy algorithm that starts with a group of reviewers for each paper and iteratively evolves it to improve the coverage of the papers’ topics by the reviewers’ expertise. We experimentally demonstrate that our method outperforms state-of-the-art approaches w.r.t several standard quality measures.     

Indices of novelty for emerging topic detection

Emerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research trends and topics. The current methods of text mining and data mining used for this purpose focus only on the frequency of which subjects are mentioned, and ignore the novelty of the subject which is also critical, but beyond the scope of a frequency study. This work tackles this inadequacy to propose a new set of indices for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). This new set of indices is created based on time, volume, frequency and represents a resolution to provide a more precise set of prediction indices. They are then utilized to determine the detection point (DP) of new emerging topics. Following the detection point, the intersection decides the worth of a new topic. The algorithms presented in this paper can be used to decide the novelty and life span of an emerging topic in a specific field. The entire comprehensive collection of the ACM Digital Library is examined in the experiments. The application of the NI and PVI gives a promising indication of emerging topics in conferences and journals.     

基于Topic Maps技术的历史文献分类组织

为解决图书馆特色文献数据库建设及特色文献检索服务的问题,本文选取"辛亥革命"历史文献资源作为研究对象,利用TopicMaps技术整合了相关网络资源,分析了历史文献分类组织中的"主题"选取原则,定义了各"主题"文献资源之间的"关联"关系,结合Ontopia主题图工具软件完成了历史文献资源的网络化、系统化组织,并展示了组织效果。     

TPI系统在高校calis专题特色数据库建设中的应用

本文介绍了CALIS专题特色库建设的意义和清华同方TPI系统在此项目中的作用,着重分析了TPI系统的六大功能模块:数据库创建、数字化加工、数据采集、数据加工、数据发布和数据备份,详细阐述了各模块的具体功能及工作流程.文章还对特色数据库建设中涉及的相关规范进行了阐述.     

基于事务标识列表的关联规则挖掘算法

设计并采用Java语言实现基于事务数据库标识列表的频繁项集的产生算法——TidlistApriori。通过与采用Hash-Tree的Apriori算法进行比较,表明TidlistApriori能够提高频繁项集的产生效率,可以成为主题关联挖掘的有效算法工具。     

The Impact of Relational Characteristics and Reasons for Topic Avoidance on Relational Closeness

Although research suggests that the act of topic avoidance itself has consequences for romantic partners, it seems equally likely that the antecedent conditions underlying these decisions to avoid certain topics also lead to important relational outcomes. Framed by communication boundary management theory, relationship characteristics, motivation for topic avoidance, and implications for relational closeness were examined. Findings indicate that relational commitment characteristics and individuals' reported rationale for topic avoidance are related to perceptions of relational closeness. These results illustrate the importance of understanding the underlying factors that motivate the decision to engage in topic avoidance and their impact on romantic relationships.     

Attachment Dimensions and the Four Horsemen of the Apocalypse

Gottman (1993 Gottman , J. M. ( 1993 ). A theory of marital dissolution and stability . Journal of Family Psychology , 7 , 57 – 75 .[Crossref] , [Google Scholar], 1994a, 1994b) identified 4 types of conflict behaviors (criticism, defensiveness, contempt, and stonewalling) that are so relationally destructive that he labeled them “the four horsemen of the apocalypse.” This study argues that it is important to identify antecedents of these kinds of communication behaviors, and assesses the degree to which attachment orientations are useful predictors of them.

Data from 170 individuals in established romantic relationships were used to test proposed associations between attachment orientations (anxiety and avoidance) and questionnaire measures of criticalness, defensiveness, contemptuousness, and stonewalling. Attachment orientations predicted an average of 22% of the variance in the criterion variables above and beyond the variance explained by relational satisfaction, with attachment anxiety being an especially potent predictor. The findings suggest that individuals whose attachment orientations reflect fears of abandonment and rejection may tend to enact conflict behaviors that increase the chances of their concerns becoming reality.     

Leveraging relevance cues for language modeling in speech recognition

Language modeling (LM), providing a principled mechanism to associate quantitative scores to sequences of words or tokens, has long been an interesting yet challenging problem in the field of speech and language processing. The n-gram model is still the predominant method, while a number of disparate LM methods, exploring either lexical co-occurrence or topic cues, have been developed to complement the n-gram model with some success. In this paper, we explore a novel language modeling framework built on top of the notion of relevance for speech recognition, where the relationship between a search history and the word being predicted is discovered through different granularities of semantic context for relevance modeling. Empirical experiments on a large vocabulary continuous speech recognition (LVCSR) task seem to demonstrate that the various language models deduced from our framework are very comparable to existing language models both in terms of perplexity and recognition error rate reductions.