基于事件本体的突发事件领域知识建模

基于本体的知识建模方法有很多,在某些特定领域采用传统的本体建模方法存在着一些不足。以突发事件领域为例,提出了基于事件本体的知识建模方法。该模型分为上层事件类、下层事件类和事件实例,上层事件类描述的抽象的事件的分类体系,下层事件类是通过事件类关系组成的事件格结构。该模型不仅可以描述事件的时间、地点、对象等要素,还能描述事件类之间的关系。采用本体建模工具Protégé来构建突发事件领域本体,并以“恐怖袭击”作为实例验证了该模型的可用性。研究结果表明,该模型可以清晰地描述事件类的完整性,语义清晰,扩展性强。     

移动信息服务中用户兴趣建模研究

本文充分考虑到移动设备的特点,对移动环境下用户兴趣模型的建立和更新方法进行了详细论述。通过爬取用户已下载浏览的WAP页面,分析用户对Wap页面的兴趣度,挖掘用户兴趣。基于ODP建立用户兴趣领域本体,采用基于领域本体的加权关键词用户兴趣表示方法。该模型能准确描述移动用户的兴趣及其动态变化过程,为移动个性化服务打下基础。     

基于多维度权重动态更新的用户兴趣模型

面向个性化电影推荐领域,提出一种基于多维度权重动态更新的用户兴趣模型。将电影分成演员、导演、类别、地区和时间5个维度,分别计算电影在这些维度上的相似度。采用归一化方法将电影之间的相似度转化为用户兴趣模型中的多维度权重,并应用TF-IDF算法计算各维度中特征词的权重,从而实现电影各维度权重及其特征词权重的动态更新。利用基于内容的推荐算法,在MovieLens数据集进行实验,结果表明,该模型具有较高的推荐准确率和召回率,并且能够发现用户对电影维度的偏好,解决用户兴趣漂移问题。     

个性化推荐服务中用户兴趣模型研究

本文提出了一种利用用户浏览页面集的内容信息和浏览行为信息,隐式地创建用户兴趣描述文件的方法。通过对用户浏览的web页面进行兴趣度分析,并与对用户浏览网页时的浏览行为分析相合,得到了用特征矩阵表示的用户兴趣模型。并采用层次聚类算法和k-means聚类算法相结合的综合聚类算法进行聚类,得到用兴趣分类树表示的用户兴趣模型。由于采用的是隐式创建用户描述文件的方法,减少了因用户参于而带来的系统噪声,保证了所创建的用户兴趣模型的准确性。     

基于用户空间位置评分云模型的Web服务协同过滤推荐算法

Web服务作为无形的产品,不具备真实环境下的空间地理位置坐标,针对服务推荐中无法衡量用户群体与Web服务之间的距离位置关系,造成用户相似度计算失衡,导致推荐不准确等问题,提出了基于用户空间位置评分云模型的Web服务协同过滤推荐算法.首先基于用户群体的行为数据量化Web服务的热度区域,通过空间位置量化评分描述用户对于Web服务的兴趣偏好;其次利用云模型来描述每个用户空间行为评分的整体特征,设计了云模型间相似贴近度的计算方法,基于该方法提出了一种用户差异程度系数评估算法,并作为调控系数优化了皮尔森相似度量;最后通过协同过滤找出用户感兴趣的Web服务.实验结果表明该算法使得用户行为偏好的区域划分更加精确,在推荐准确率上明显提高,为基于位置的Web服务推荐提供新颖的方案.     

基于博弈论的SON带宽分配研究

描述一种SON结构,基于博弈论方法研究网络带宽分配问题,建立双层规划模型,模型中考虑各种因素例如服务QoS、带宽成本以及流量需求。上层规划是以SON利益最大化为目标而建立的,下层规划是SON在弹性需求下的Wardrop用户均衡模型。用下降方向法和差分灵敏度分析法结合的启发式方法求解模型。     

基于用户操作行为的兴趣度的分析与计算

在互联网的背景下,用户检索行为所体现的兴趣是零散的、分布的.利用一个群集模型来综合这些分布的信息,对个性化服务也会提供帮助.通过对单个用户行为的分析,提出了一种基于操作行为的兴趣度的计算方法,可以有效地计算出该用户对当前内容的兴趣度的基值,并最终为用户兴趣群集模型中各个结点的兴趣度的值的计算提供重要依据.     

基于RSS信息源的用户兴趣建模与更新

互联网迅速发展，个性化信息服务成为研究的热点之一。RSS标准提供了结构化的信息模式，便于信息搜索和概要浏览。该文针对基于RSS标准的新闻源，根据用户点击等隐式信息，通过文本相似判定，自动聚类形成用户兴趣子类。用户模型节点、信息类别和用户兴趣子类构成了三层结构的树状用户兴趣模型。信息类别与用户兴趣子类均有对应的兴趣度。用户模型的更新是通过用户兴趣子类的更新与相关兴趣度的更新完成的。通过此模型进行信息推荐还要保证适当的信息冗余度。该模型的个性化程度高且更新效果好。     

基于概念模型的个性化搜索引擎研究

在对用户兴趣模型探讨的基础上,提出了一种基于概念的用户兴趣模型,用于区别用户兴趣的大小.讨论了基于链接的查询聚类算法,并针对该算法的不足提出了一种基于概念的聚类算法,该算法根据用户兴趣模型建立查询-概念二分图,然后计算图中查询顶点间的概念相似度,并将概念相似度最高的查询顶点进行合并以实现聚类.设计实现了一个基于Web数据挖掘的个性化搜索引擎系统,对系统的个性化查询进行了测试,并对比分析了链接聚类和概念聚类的实验结果.     

用户个性化推荐系统的设计与实现

为实现个性化服务,理解用户兴趣就成了提供服务的关键任务,因此,提出了隐性采集用户浏览内容、用户浏览时间和用户操作时间的信息方法,通过对网络爬虫程序抓取的网页进行内容清洗提取出主要内容之后,利用VSM建立文档模型,并采用SVM分类方法建立推荐库.基于从客户端采集的用户兴趣信息建模,以及根据该模型和推荐库的相似度,给用户推荐信息.此外,给出了基于该模型的推荐原型系统的实现,使用查准率来评价该系统.试验结果表明,系统较好地实现了基于用户兴趣来推荐阅读的信息.     

一种基于领域本体的用户建模方法

针对当前大多数个性化服务系统的不足,以旅游领域为背景,提出了一种新的基于本体的用户模型构建方法,利用领域本体中的概念、实例和属性描述用户兴趣特征,实现了在语义层次上理解用户兴趣。实验表明,该方法能有效提高用户模型的质量。     

Learning and inferencing in user ontology for personalized Semantic Web search

User modeling is aimed at capturing the users’ interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users’ interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user’s interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.     

个性化流媒体服务中基于行为分析的用户兴趣建模

提出一种基于行为分析的用户兴趣建模方法,计算用户短期兴趣和长期兴趣,以满足流媒体服务中的个性化需求,提高服务的效率。该方法在综合分析用户历史行为的基础上,考虑了不同行为与用户兴趣偏向之间的关系。实验结果表明,该方法能够较为准确地评估用户兴趣偏好。     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval.On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience.In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories.In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.     

Automatic extraction of persistent topics from social text streams

Microblogging services allow users to publish their thoughts, activities, and interests in the form of text streams and to share them with others in a social network. A user’s text stream in a microblogging service is temporally composed of the posts the user has written or republished from other socially connected users. In this context, most research on the microblogging service has primarily focused on social graph or topic extraction from the text streams, and in particular, several studies attempted to discover user’s topics of interests from a text stream since the topics play a crucial role in user search, friend recommendation, and contextual advertisement. Yet, they did not yet fully address unique properties of the stream. In this paper, we study a problem of detecting the topics of long-term steady interests to a user from a text stream, considering its dynamic and social characteristics, and propose a graph-based topic extraction model. Extensive experiments have been carried out to investigate the effects of the proposed approach by using a real-world dataset, and the proposed model is shown to produce better performance than the existing alternatives.     

网格工作流访问控制研究

工作流管理系统中采用适当的访问控制机制能够确保各项任务被合法的用户或代表一定用户利益的合法程序执行。本文对网格工作流访问控制做一些非形式化研究,提出了基于服务的网格工作流动态访问控制模型,将工作流中任务和服务相联系,实现了工作流中访问控制的最小特权原则。     

Building Web Knowledge Flows based on Interactive Computing with Semantics

Web personalized services alleviate the burden of information overload by providing right information which meets individual user’s needs. How to obtain and represent knowledge needed by users is a key issue. This paper presents Web Knowledge Flow (WKF) to represent the specific knowledge on Web pages and a model of Interactive Computing with Semantics (ICS) to provide a feasible means of generating WKF. Objective WKF (OWKF) and Real-time WKF (RWKF) are firstly proposed to satisfy staged and real-time user interests. Secondly, the generation algorithm of WKF is proposed based on Semantics Link Network. Thirdly, “interactive point” is introduced to detect the moment of user interests change to ensures the dynamics of WKF. Experimental results demonstrate that ICS can effectively capture the change of user interests and the generated WKF can satisfy user requirements accurately.     

基于情景和浏览内容的层次性用户兴趣建模

用户兴趣建模是个性化服务的核心,考虑到情景信息对用户偏好的影响,对融和情景信息的用户行为日志数据进行深入研究,提出了一种基于情景信息的用户兴趣建模方法.该方法首先通过计算情景相似度来获得用户当前情景的近似情景集;对“用户-兴趣项-情景”三维模型采用情景预过滤的方法降维处理.然后根据用户浏览内容得到用户兴趣主题,分析页面内容得到每种主题的兴趣关键词,建立基于层次向量空间模型的用户兴趣模型.实验结果表明,本文提出的基于情景信息的用户兴趣模型对用户兴趣的预测误差控制在9%以内,是有效的.     

网络智能信息服务系统的设计与实现

本文介绍了基于Agents合作和移动Agent的思想,实现具有学习能力、自主式的网络智 能信息服务系统的方法,以便对Internet信息自动收集和过滤,从而把用户从大量、分散、 复杂的电子信息中解脱出来,节约用户时间,提高效率．