Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.     

User modeling in dialog systems: Potentials and hazards

In order to be capable of exhibiting a wide range of cooperative behavior, a computer-based dialog system must have available assumptions about the current user's goals, plans, background knowledge and (false) beliefs, i.e., maintain a so-called user model. Apart from cooperativity aspects, such a model is also necessary for intelligent coherent dialog behavior in general. This article surveys recent research on the problem of how such a model can be constructed, represented and used by a system during its interaction with the user. Possible applications, as well as potential problems concerning the advisability of application, are then discussed. Finally, a number of guidelines are presented which should be observed in future research to reduce the risk of a potential misuse of user modeling technology.     

Student modeling to support multiple instructional approaches

Intelligent computer-assisted instruction (ICAI) systems have continually sought increased flexibility to respond appropriately to the multi-faceted interests of students. Research on theImage student modeler of theGuidon2 ICAI system has developed amultiple-anticipation approach to plan generation and interpretation that directly meets a wide range of communication goals: providing information support, encouraging exploration with interesting elaborations, recognizing strategic mistakes in actions and plans, evaluating success in domain tasks, diagnosing misconceptions, and recommending improvements for mistakes.In order to meet pragmatic system constraints,Image must provide its full range of advice simultaneously, continually, and quickly. It drops many of the simplifying assumptions typically used by plan recognition user modelers, including assumptions of closed-world knowledge and of the user's correctness, cooperation, and unified goal. To maintain efficiency for dynamic plan recognition,Image relies instead on two assumptions of cognitive economy, contextualrelevance and conceptualeasiness, which are operational forms of Grice's maxims of relation and quantity. Its multiple-anticipation approach to plan management provides all of the requisite information together and allows incremental updating and relaxation methods of interpretation, even when students are shifting focus frequently.Robert London's doctoral research was on student modeling and instructional planning in theGuidon2 ICAI system, which developed several ways for students to learn by interactive development of qualitative models. His PhD disseration at Stanford University is entitledStudent Modeling with Multiple Viewpoints by Plan Inference. He has published research papers on student modeling, plan recognition, and automated learning. Recently he has led R&D projects at Cimflex Teknowledge in Palo Alto, California, in knowledge-based tools and training. His current interests include use of simulation systems for cooperative learning and design, especially with object-oriented tools and multi-media presentation systems.     

A dempster-shafer approach to modeling agent preferences for plan recognition

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such additional information provides a basis for ranking the hypotheses so that the best one can be selected whenever the system is forced to react (e.g., to provide help to the user of a software system to accomplish his goals). Furthermore, hypotheses with low valuations can be excluded from considerations at an early stage. In this paper, an approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented. It relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.     

基于行为预测的多智能体协作模型

智能体的行为预测是多智能体系统中的一个具有挑战性的问题。机器人足球是一个典型的多智能体系统,论文对多智能体系统中的行为预测进行了研究,概述了RoboCup中几种典型的智能体行为预测方法和协作模型,在此基础上采用基于行为的混合预测方法,建立了一种基于行为预测的多智能体协作模型。该协作模型已应用于CSU_YunLu2003仿真球队中,在RoboCup仿真比赛中证明了该协作模型的有效性。     

对话推荐算法研究综述

推荐系统是一种通过理解用户的兴趣和偏好帮助用户过滤大量无效信息并获取感兴趣的信息或者物品的信息过滤系统.目前主流的推荐系统主要基于离线的、历史的用户数据,不断训练和优化线下模型,继而为在线的用户推荐物品,这类训练方式主要存在3个问题:基于稀疏且具有噪声的历史数据估计用户偏好的不可靠估计、对影响用户行为的在线上下文环境因素的忽略和默认用户清楚自身偏好的不可靠假设.由于对话系统关注于用户的实时反馈数据,获取用户当前交互的意图,因此“对话推荐”通过结合对话形式与推荐任务成为解决传统推荐问题的有效手段.对话推荐将对话系统实时交互的数据获取方式应用到推荐系统中,采用了与传统推荐系统不同的推荐思路,通过利用在线交互信息,引导和捕捉用户当前的偏好兴趣,并及时进行反馈和更新.在过去的几年里,越来越多的研究者开始关注对话推荐系统,这一方面归功于自然语言处理领域中语音助手以及聊天机器人技术的广泛使用,另一方面受益于强化学习、知识图谱等技术在推荐策略中的成熟应用.将对话推荐系统的整体框架进行梳理,将对话推荐算法研究所使用的数据集进行分类,同时对评价对话推荐效果的相关指标进行讨论,重点关注于对话推荐系统中的后...     

自主微小型移动机器人的协作学习

自主微小型移动机器人的协作学习研究是多智能体机器人系统理论的主要研究方向。因为单个微小型移动机器人能力有限,所以机器人之间的协作在某些重要的基础工业和生物医学领域方面显得非常重要。该文介绍了几种用于协作学习的方法并且比较了它们之间的优点和缺点。最后,简要介绍了一些研究工作。     

The user modeling shell system BGP-MS

BGP-MS is a user modeling shell system that can assist interactive software systems in adapting to their current users by taking the users' presumed knowledge, beliefs, and goals into account. It offers applications several methods for communicating observations concerning the user to BGP-MS, and for obtaining information on currently held assumptions about the user from BGP-MS. It provides a choice of two integrated formalisms for representing beliefs and goals, and includes several types of inferences for drawing additional assumptions based on an initial interview, observed user actions, and stereotypical knowledge about pre-defined user subgroups. BGP-MS is a customizable software system that is independent from applications, operates concurrently with them, and interacts with them through inter-process communication. For tailoring BGP-MS to a specific application domain, the developer must select those components of BGP-MS that are needed in this domain and fill them with relevant domain-dependent user modeling knowledge. This paper first summarizes the user modeling services that BGP-MS provides to application programs at runtime. It discusses the representational and inferential foundations that determine the scope and the limits of these services, and also gives a detailed example illustrating the interaction between the various system components. It describes interfaces that are available to application developers for tailoring BGP-MS to the specific user modeling needs of their application domains. Finally, it compares the system with all other major user modeling shell systems, and describes a first application that employs BGP-MS for adapting hypertext to users' terminological knowledge.The managing UMUAI editor for this paper was Sandra Carberry, University of Delaware.     

一种面向个性化服务的客户端细粒度用户建模方法

用户建模是实现个性化服务的关键技术。本文分析了二类用户建模存在的问题,给出了细粒度用户模型的定义,结合用户的背景知识,提出了一种客户端细粒度用户建模方法 。采用词频方法选择的特征子集和改进的k近邻分类器构成用户模型。本文的细粒度用户建模方法不需要用户的频繁交互．也不必对用户兴趣作推测,具有更好的系统亲和力
和性能。实验表明,当特征个数为40时,构建的细粒度用户模型的分类精度可达90％以上;在细粒度用户模型中,大量的特征对用户模型没有意义。     

A Study on Automatic Classification of Users’ Desktop Interactions

Knowledge workers frequently change activities, either by choice or through interruptions. With an increasing number of activities and activity switches, it is becoming more and more difficult for knowledge workers to keep track of their desktop activities. This article presents our efforts to achieve activity awareness through automatic classification of user's everyday desktop activities. For getting a deeper understanding, we investigate performance of various classifiers with respect to discriminative power of time-, interaction-, and content-based feature sets for different work scenarios and users. Specifically, by viewing an activity as a sequence of desktop interactions we present (1) a methodology for translating a user's desktop interactions into activities, (2) evaluation of the discriminative power of different activity features and feature types, and (3) analysis of supervised classification models for classifying desktop activity under two different scenarios, i.e., an activity-centric scenario and a user-centric scenario. The experiments are carried out on a real-world dataset, and the results show satisfactory accuracy using relatively few and simple types of features.     

基于动态模型的系统架构及建模平台实现

传统的基于静态模型的系统开发方法难以解决业务规则的快速扩展及变更带来的资源浪费等问题,工作流支持对工作内容进行良好的划分,并进行具体部署。结合SOA架构的业务流程再造能力,提出一种新的以三层模型为核心的支持快速、平滑、可重用的应用系统开发架构,对流程引擎和建模平台完成详细描述和设计,完成建模平台搭建工作,并以卡银行业务为例进行了基于三层模型的建模。结果表明,该系统开发架构具有很好的业务流程再造能力,可保证业务系统的持续稳定,达到提高生产组织水平和工作效率的目的。     

一个支持自动协作式测量的大规模网络测量系统

设计并实现了一个大规模网络测量系统FOX，该系统除具备较好的通用性、可扩展性特点之外，以一种安全高效的方式实现了对自动协作式测量的支持。目前该系统已经在实际网络测量中得到了应用。     

用户画像技术在产品营销中的应用

随着人工智能和大数据技术的不断发展,对信息的获取方式也在发生着变化,消费者希望用最少的时间成本获取尽量多的商品信息。如何在产品的营销过程中实现对目标用户的精准定位,基于用户画像技术的推荐系统就是目前所采用的有效方法之一。本文对互联网环境下在产品的推广和营销过程中如何利用用户画像技术,有针对性地选择推荐系统算法进行了探讨和研究。     

Constructing and Utilizing a Model of User Preferences in Collaborative Consultation Dialogues

A natural language collaborative consultation system must take user preferences into account. A model of user preferences allows a system to appropriately evaluate alternatives using criteria of importance to the user. Additionally, decision research suggests both that an accurate model of user preferences could enable the system to improve a user's decision-making by ensuring that all important alternatives are considered, and that such a model of user preferences must be built dynamically by observing the user's actions during the decision-making process. This paper presents two strategies: one for dynamically recognizing user preferences during the course of a collaborative planning dialogue and the other for exploiting the model of user preferences to detect suboptimal solutions and suggest better alternatives. Our recognition strategy utilizes not only the utterances themselves but also characteristics of the dialogue in developing a model of user preferences. Our generation strategy takes into account both the strength of a preference and the closeness of a potential match in evaluating actions in the user's plan and suggesting better alternatives. By modeling and utilizing user preferences, our system is able to fulfill its role as a collaborative agent.     

基于数学建模理论的问题研究与分析

数学模型是架于数学与实际问题之间的桥梁,在数学发展的进程中无时无刻不留下数学模型的印记.数学模型是一种数学的思维方法,用数学语言和方法来抽象简化实际问题,以便于实际问题的解决.数学建模不仅是应用数学解决实际问题的重要工具,而且是揭示基本自然规律,产生新的数学思想和方法的重要途经.文章用实例介绍了数学建模理论及其应用.     

基于需求的酒店管理系统的建模与实现

在软件开发过程中,利用建模语言来有效地建立系统模型是非常关键的。为了提高软件开发效率,本文采用面向对象系统建模方法,结合酒店管理系统的开发,探讨基于需求的系统建模,设计并实现军山酒店客户关系管理系统。实践表明,基于需求的系统建模能够缩短软件开发周期,节约软件开发成本,从而提高软件开发效率。     

Database support for solid modeling

Geometric solid modeling plays one of the most important roles in CAE (Computer Aided Engineering) activities. In order that geometric solid modeling functions sufficiently in the CAE environment, an EDBMS (Engineering Database Management System) supporting solid modeling is highly desirable. In this paper we present a new approach to the design of a geometric solid database system. In the proposed approach an EDBMS with capability of object-oriented accessing and manipulation is developed, and a solid sharing model that is a scheme for representing and manipulating a collection of interrelated solids is proposed. The solid sharing model is used as a basis for designing and manipulating a solid database managed by the EDBMS. Also explored is a mechanism for supporting a long transaction which generates many intermediate designs.     

基于Agent的建模技术研究

基于Agent的建模技术是对复杂系统进行问题求解的一种新方法.在面向Agent方法的基础上提出了基于Agent的建模技术方法,并针对这一技术方法从系统组织的角度进行了系统性的研究,详细分析了用这一方法建模的主要流程,还首次将这一建模技术运用于网络坦克作战系统-协作故障诊断与技术维护系统进行实例分析,根据系统的组织结构和组织目标,构建了3类Agent:系统接口Agent,故障分析Agent和故障区域分析Agent,系统通过3类Agent间的交互协作实现问题求解.最后讨论了应用该技术应注意的主要问题.     

Numerical uncertainty management in user and student modeling: An overview of systems and issues

A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the Dempster-Shafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections focuses on one of these paradigms. It first introduces the basic concepts by showing how they can be applied to a relatively simple user modeling problem. It then surveys systems that have applied techniques from the paradigm to user or student modeling, characterizing each system within a common framework. The final main section discusses several aspects of the usability of these techniques for user or student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results.     

基于UML的嵌入式建模方法改进策略研究

统一建模语言UML(unified modeling language)在嵌入式系统设计建模中已经获得了广泛的承认,有很多成功的应用.但UML在嵌入式建模中存在时间约束描述能力不强和所建模型形式化复杂、验证难及模型重用性不高等问题.针对这些问题提出了一种改进策略:定义实时语义和映射规则,建立实时描述模式模板,使用模板中实时描述模式描述时间约束信息.改进后的方法能可视化地分析模型、纠正错误和简单地进行形式化转换,能利用支撑工具对模型进行验证,较好地解决了UML在嵌入式系统建模中存在的问题.