Machine Learning for User Modeling

At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.     

A Movie Recommendation System – An Application of Voting Theory in User Modeling

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.     

Latent Semantic Analysis for User Modeling

Latent semantic analysis (LSA) is a tool for extracting semantic information from texts as well as a model of language learning based on the exposure to texts. We rely on LSA to represent the student model in a tutoring system. Domain examples and student productions are represented in a high-dimensional semantic space, automatically built from a statistical analysis of the co-occurrences of their lexemes. We also designed tutoring strategies to automatically detect lexeme misunderstandings and to select among the various examples of a domain the one which is best to expose the student to. Two systems are presented: the first one successively presents texts to be read by the student, selecting the next one according to the comprehension of the prior ones by the student. The second plays a board game (kalah) with the student in such a way that the next configuration of the board is supposed to be the most appropriate with respect to the semantic structure of the domain and the previous student's moves.     

面向用户偏好建模的个性化对话推荐算法

推荐系统的目标是从物品数据库中，选择出与用户兴趣偏好相匹配的子集，缓解用户面临的“信息过载”问题。因而近年来推荐系统越来越多地应用到电商、社交等领域，展现出巨大的商业潜力。传统推荐系统中，系统对用户的认知往往来源于历史交互记录，例如点击率或者购买记录，这是一种隐式用户反馈。对话推荐系统能够通过自然语言与用户进行多轮对话，逐步深入挖掘其兴趣偏好，从而向对方提供高质量的推荐结果。相比于传统推荐系统，对话推荐系统主要有两方面的不同。其一，对话推荐系统能够利用自然语言与用户进行语义上连贯的多轮对话，提升了人机交互中的用户体验；其二，系统能够询问特定的问题直接获取用户的显式反馈，从而更深入地理解用户兴趣偏好，提供更可靠的推荐结果。目前已经有不少工作在不同的问题设定下对该领域进行了探索，然而尽管如此，这些工作仍仅局限于关注当前正在进行的对话，忽视了过去交互记录中蕴涵的丰富信息，导致对用户偏好建模的不充分。为了解决这个问题，本文提出了一个面向用户偏好建模的个性化对话推荐算法框架，通过双线性模型注意力机制与自注意力层次化编码结构进行用户偏好建模，从而完成对候选物品的排序与推荐。本文设计的模型结构能够在充分利用用户历史对话信息的同时，权衡历史对话与当前对话两类数据的重要性。丰富的用户相关信息来源使得推荐结果在契合用户个性化偏好的同时，更具备多样性，从而缓解“信息茧房”等现象带来的不良影响。基于公开数据集的实验表明了本文方法在个性化对话推荐任务上的有效性。     

Generic User Modeling Systems

The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user. Several implemented research prototypes and commercial systems are briefly described.     

User Intention Modeling in Web Applications Using Data Mining

The problem of inferring a user's intentions in Machine–Human Interaction has been the key research issue for providing personalized experiences and services. In this paper, we propose novel approaches on modeling and inferring user's actions in a computer. Two linguistic features – keyword and concept features – are extracted from the semantic context for intention modeling. Concept features are the conceptual generalization of keywords. Association rule mining is used to find the proper concept of corresponding keyword. A modified Naïve Bayes classifier is used in our intention modeling. Experimental results have shown that our proposed approach achieved 84% average accuracy in predicting user's intention, which is close to the precision (92%) of human prediction.     

User Modeling for Personalized City Tours

Several current support systems for travel and tourism are aimed at providing information in a personalized manner, taking users' interests and preferences into account. In this vein, personalized systems observe users' behavior and, based thereon, make generalizations and predictions about them. This article describes a user modeling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users. The system is open and compliant with major standards, allowing it to be easily accessed by clients that need personalization services.     

Fuzzy User Modeling for Information Retrieval on the World Wide Web

Information retrieval from the World Wide Web through the use of search engines is known to be unable to capture effectively the information needs of users. The approach taken in this paper is to add intelligence to information retrieval from the World Wide Web, by the modeling of users to improve the interaction between the user and information retrieval systems. In other words, to improve the performance of the user in retrieving information from the information source. To effect such an improvement, it is necessary that any retrieval system should somehow make inferences concerning the information the user might want. The system then can aid the user, for instance by giving suggestions or by adapting any query based on predictions furnished by the model. So, by a combination of user modeling and fuzzy logic a prototype system has been developed (the Fuzzy Modeling Query Assistant (FMQA)) which modifies a user's query based on a fuzzy user model. The FMQA was tested via a user study which clearly indicated that, for the limited domain chosen, the modified queries are better than those that are left unmodified. Received 10 November 1998 / Revised 14 June 2000 / Accepted in revised form 25 September 2000     

产品数据管理系统的用户权限管理

通过对产品数据管理系统的分析，本文探讨了产品数据管理系统中数据、工作流程、活动、操作和角色之间的关系，提出了基于数据、工作流程、活动、操作和角色的用户权限管理的建模方法。通过在某航空企业PDM系统中建立用户权限管理模型的应用，证明了这种方法具有简单、规范、有效等特点，井可广泛应用于其它类型的复杂信息系统中。     

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。     

Vocal Access to a Newspaper Archive: Assessing the Limitations of Current Voice Information Access Technology

This paper presents the design and the current prototype implementation of an interactive vocal Information Retrieval system that can be used to access articles of a large newspaper archive using a telephone. The implementation of the system highlights the limitations of current voice information retrieval technology, in particular of speech recognition and synthesis. We present our evaluation of these limitations and address the feasibility of intelligent interactive vocal information access systems.     

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.     

基于MVC模式的Web用户界面建模

分析了企业信息系统的Web用户界面开发特性,研究了当前主流前端框架的MVC(模型-视图-控制器)设计模式应用,针对Web前端开发需求及最佳实践方法,提出了一种符合MVC用户界面开发最佳实践的组件化Web用户界面建模方法.设计一套抽象和描述Web用户界面组件的UML(统一建模语言)概要文件,从界面数据模型、界面组件模型到界面交互模型等3个方面完成Web用户界面建模.以一个装修行业定制型ERP(企业资源计划)系统为例,通过用户界面建模实践,验证了本建模方法的可行性、易用性和有效性.     

一种基于本体的个性化模式库建模方法

搜索引擎的“千人一面”为人们信息检索时带来了很大的烦恼,个性化模式库技术的引入解决了这个问题,使得搜索引擎能够很好地满足人们的个性化、智能化需求.提出一种基于本体的个性化模式库建模方法,通过树图和空间图相结合的方法来建模,在空间图中建立本体节点,并引入区间值模糊集理论,同时给出相关定义和公式,在对该方法进行理论分析的基础上,设计了一个实现算法.这种建模方法对改进传统树形建模的不足有一定益处,更利于建立、使用和完善用户个性化模式.理论分析证明,该算法具有正确性、有效性并且复杂性低的特点.     

基于用户行为分析的个人信息检索研究

个人信息检索是指个人计算机上用户搜索个人信息(通常是文档)的过程,与互联网检索相比,个人信息检索能够利用的信息很少,这使得其检索结果的排序更加困难。该文通过考察计算机上的用户行为,对个人信息检索的排序问题进行深入的研究。该文考察的用户行为主要包括用户在检索系统中的查询行为和在计算机上的文件访问行为。该文一方面通过查询行为数据训练出结果排序函数,另一方面通过文件访问行为数据获取文件自身的权重,最后利用统计学习方法结合这两类行为的计算结果。实验结果表明,该文提出的方法好于传统的TFIDF排序方法。     

The Advantages of Designing Adaptive Business Agents Using Reputation Modeling Compared to the Approach of Recursive Modeling

Adaptive business agents operate in electronic marketplaces, learning from past experiences to make effective decisions on behalf of their users. How best to design these agents is an open question. In this article, we present an approach for the design of adaptive business agents that uses a combination of reinforcement learning and reputation modeling. In particular, we take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possibility of dishonest agents in the marketplace. Our buying agents exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. We then experimentally compare the performance of our agents with those designed using a recursive modeling approach. We are able to show that agents designed according to our algorithms achieve better performance in terms of satisfaction and computational time and as such are well suited for the design of electronic marketplaces.     

Information Filtering: A New Two-Phase Model Using Stereotypic User Profiling

Computer users often experience the lost in informationspace syndrome. Information filtering suggests a solution based onrestricting the amount of information made available to users. Thisstudy suggests an advanced model for information filtering which isbased on a two-phase filtering process. The user profiling in themodel is constructed on the basis of the user's areas of interestand on sociological parameters about him that are known to thesystem. The system maintains a database of known stereotypes thatincludes rules on their information retrieval needs and habits.During the filtering process, the system relates the user to one ormore stereotypes and operates the appropriate stereotypic rules.     

An efficient hybrid similarity measure based on user interests for recommender systems

Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.     

个性化服务中的用户建模技术

作为个性化服务的基础和核心,用户建模的质量直接关系到个性化服务的质量.文章将用户建模的过程分为5个关键模块:输入、输出、建模时间、建模的对象、建模算法,并围绕这5个方面,对用户建模当前的研究现状、所面临的关键议题进行了系统的论述.其中,输入模块为用户模型的建立提供了必要的数据源,输出模块则描述用户模型的表示方式,建模时间描述了建模的时间长度和更新方式,建模的对象描述了对谁进行建模,建模算法则描述了几种典型的建模方法.最后对用户建模的技术发展进行了展望.