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.     

一种基于用户情境聚类的个性化推荐算法

面向基于情境感知的推荐问题,提出一种基于用户情境聚类的个性化推荐算法。该算法利用情境预过滤的思想,首先运用模糊聚类的方法对历史数据集中用户的情境进行聚类,构造与当前用户情境相似度较高的用户集合,再与传统的基于用户的协同过滤算法相结合进行个性化推荐。实验采用公开数据集,结果表明该算法在多维情境信息条件下可用,并且推荐准确度要高于传统协同过滤算法,在聚类粒度不同的情况下对推荐结果也会产生不同的影响。     

基于语义Web技术的上下文感知的智能移动服务

语义Web技术应用于上下文感知的智能移动服务，通过构建上下文信息本体，使得移动服务的实体之间可以进行上下文信息共享和语义互操作，并进行上下文信息推理，实现智能服务。本文首先介绍了语义Web及本体技术，其次阐述了语义Web技术应用于上下文感知的移动服务，然后详细分析了智能移动服务中的上下文信息本体构建，包括通用的上下文信息本体、用户概况本体、情境本体以及服务本体等，接着介绍了相关的研究项目，最后进行展望和总结。     

融合用户会话数据的上下文感知推荐算法

针对现有会话推荐算法未充分考虑用户的上下文信息的现状,为增强基于会话的推荐算法的个性化推荐效果,提出一种融合用户会话数据的上下文感知推荐算法。将上下文信息通过embedding映射成低维实数向量特征,通过Add、Stack、MLP三种组合方式将低维向量特征融入到基于会话的循环神经网络推荐模型,设计了基于BPR的损失函数动态刻画会话点击序列的用户偏好,以提升个性化推荐能力。在Adressa数据集上的实验表明,所提算法相比基线算法GRU4REC,在指标Recall@20上提高了3.2%,MRR@20上提高了27%。     

个性化检索中的用户ontology及其构建

为使用户能够从信息庞杂的网络中方便准确地找到自己所需的信息，在传统检索技术的基础上，结合ontology及个性化检索技术的研究，提出个性化检索中用户ontology的概念，讨论了基于行为的用户描述文件的建立，并制定了个性化检索中语义关系的提取规则。最后在此基础上给出了用户ontology的构建方法，并通过比较实验说明了用户ontology在个性化检索中的有效性。     

Agent Models and Different User Ontologies for an Electronic Market Place

In this paper the agent-based electronic market architecture GEMS is described. The market incorporates different user perspectives: consumers, retailers, and producers. Ontologies for the different user perspectives are included. Knowledge is included to relate information from the different perspectives; for example, evaluation knowledge that can be used to derive product evaluations in terms of user ontology from product information based on producer ontology. Agent models are used as a high-level design structure for the architecture. It is shown how this combination of agent models, ontologies and knowledge provides an adequate approach to the distributed and knowledge-intensive character of the application.     

基于时序背景LDA与协同过滤的混合推荐模型

现有的个性化推荐通常会忽略时间信息对用户行为的影响,导致预测准确性较低。本文根据用户属性信息和用户评分信息,建立基于时序背景LDA与协同过滤的混合模型(TLDA-CF)。通过离线与在线推荐提高推荐效率;根据用户对项目的评分信息,以及各时间段内项目的被访问量分别建立LDA模型,解决数据稀疏性问题;设置动态权值平衡用户选择趋势提高推荐准确性;对于没有评分信息的新用户,采用用户聚类,引用协同过滤算法预测新用户喜好,解决冷启动问题。将该算法应用在MovieLens数据集上,实验结果表示,该算法在推荐召回率和准确率、F1值上都优于传统的LDA模型。     

个性化网络信息过滤Agent的反馈评价机制

文章描述了信息过滤的作用,并介绍了一个基于Agent的万维网文档信息过滤系统。文中提出了个性化网络信息过滤Agent的结构及其实现方案,并讨论了用相关反馈评价机制更新用户兴趣模型的问题,建议用决策树从用户分类的文档集中学习用户的信息兴趣。     

Personalization and Context Management

Supporting the individual user in his working, learning, or information access is one of the main goals of user modeling. Personal or group user models make it possible to represent and use information about preferences, knowledge, abilities, emotional states, and many other characteristics of a user to adapt the user experience and support. Nowadays, the disappearing computer enables the user to access her information from a variety of personal and public displays and devices. To support a new generation of contextualized and personalized information and services, this paper addresses the problem of context management. Context management is a new approach to the design of context-aware systems in ubiquitous computing that combines personalization and contextualization. The presented framework for context management integrates user modeling and context modeling, which can benefit from each other and give rise to more valid models for personalized and contextualized information delivery. The paper will introduce a base framework and tools for designing context-aware applications and decompose the underlying framework into its foundational components. As two illustrative application cases, the paper discusses implementations of an intelligent advertisement board and an audio-augmented museum environment.     

Context-aware system for proactive personalized service based on context history

Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users’ preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users’ profile and services under the same context automatically.     

Experiences in developing mobile applications using the Apricot Agent Platform

The trend of increasing mobile services has set a new challenge for service providers: how to develop more advanced and user-friendly, context-aware and personalized mobile services for the users. Apricot Agent Platform is an agent-based software platform designed for developing context-aware and personalized mobile services. Apricot Agent Platform supports the development of user-friendly mobile services by providing tools for combining various mobile and Internet-based services. Apricot agent architecture consists of an agent platform, agents and agent containers. For the developers of mobile services, it provides built-in functionality and communication mechanism. Furthermore, this paper describes four demonstrators that are built up on the Apricot Agent Platform to evaluate the usability and efficiency of the platform in the processes of building mobile services. The outcome of the evaluation indicates relatively promising results and the further target for development is revealed.     

A middleware architecture for privacy protection

The issue of user privacy is constantly in spotlight since an ever increasing number of online services collects and processes personal information from users, in the context of personalized service provision. In fact, recent advances in mobile communications, location and sensing technologies and data processing are boosting the deployment of context-aware personalized services and the creation of smart environments; but at the same time, they pose a serious risk on individuals’ privacy rights. Although technology makes the collection of data easy, its protection against abuse is left to data protection legislation. However, the privacy requirements, other than being general and abstract terms to be regarded as legislature issues, should be brought down in technological reality and carefully accounted for in devising technical solutions. In order to limit the disclosure and avoid the misuse of personal data, this paper discusses an architectural proposal for a middleware system that will enforce protection of user privacy through technical means. The proposed architecture mediates between the users, the service providers and the law, constituting a middleware shield for individuals’ personal data.     

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.     

一种自适应语义Web检索方法

基于内容的语义Web检索只考虑内容本身,没有考虑用户的不同,不能准确反映用户需求。为此,提出一个自适应语义Web检索框架,对于Web中文文档,借助HowNet知识库给出一种本体学习方法,通过提取用户客观、显式和隐式信息建立用户信息库,并设计用户初始查询本体和个性化查询本体构建算法,从而实现用户的自适应检索。实验结果表明,该方法具有较高的检索效率。     

个性化搜索中的用户特征模型研究

用户特征的描述方式是实现个性化搜索算法的核心因素。针对传统的基于关键词向量空间模型的用户特征描述过于简单,不能全面描述用户兴趣的缺陷,将folksonomy的结构与本体概念的清晰语义相结合,提出一种多层用户特征描述方式。从用户兴趣主题、用户间关联两个不同角度,从用户生成的标签、标记的文档及主题等不同层次建立用户特征描述模型,并将其应用于个性化搜索过程的方式进行分析。同时对个性化搜索的结果评价方式、资源类型对用户特征及搜索结果的影响进行了讨论。在Delicious和Flickr两种不同类型数据集上的实验表明,所提出用户特征模型能够有效提高个性化搜索结果的性能。     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

情景感知的物联网服务推荐方法研究

如何根据用户实时的情景高效地为其推荐最为合适的物联网服务,已经成为当前服务计算与物联网领域亟需解决的关键问题之一。针对这一问题,提出了一种基于情景感知的物联网服务推荐方法。首先基于改进的FolkRank算法生成当前用户可用的物联网服务列表;之后,依据用户当前关键的情景构建用户情景信息模型,根据用户的情景模型从服务列表中筛选出最能满足用户当前情景的物联网服务。实验结果表明,所提出的情景感知的物联网服务推荐方法是可行的与有效的。     

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities.     

基于本体的上下文感知中间件框架

上下文感知是普适计算的核心技术之一,而描述和理解上下文信息是上下文感知的前提。由于上下文信息种类繁多、感知方式迥异,目前开发面向特定应用的上下文感知系统缺乏统一的机制和通用的架构,增加了系统开发的成本。引入语义Web技术,利用本体对上下文信息进行建模,采用本体描述语言描述上下文模型,提供了一个公共的上下文本体以实现多个独立开发的上下文感知系统对知识的共享和推理,构建了通用的上下文感知中间件框架,从而实现对域内上下文知识的共同理解。     

基于本体和多Agent的个性化主动服务模型

为了突破传统主动服务模型的性能瓶颈,更好地为用户提供满足个性化需求的服务,基于领域本体建立并维护了本体用户个性模型,提出了基于本体和多Agent的个性化主动服务模型,分析了模型的运行过程,详细描述了模型中各部分功能。在详细分析模型运行的关键技术之后,阐明了模型的推与拉协作运行机制,实现了用户感兴趣信息的主动推送和用户个性需求的快速检索。最后,基于模型思想的原型系统的实验结果表明,模型能有效提高服务的性能。