User modeling has made considerable progress during its existence now of more than a decade. In this paper, a survey of recent developments will be presented, which concentrates on the modeling of a user's knowledge, plans, and preferences in a domain, on the exploitation of new sources of information about the user, on issues of representation, inference and revision, on user modeling shell systems and servers, and on the verification of the practical utility of user models. Research trends and research deficiencies in these areas will be outlined, and potential risks described. 1. Introduction User modeling has made considerable progress during its existence now of more than a decade. Particularly in the last few years, the need for software systems to automatically adapt to their current users has been recognized in many application areas. Consequently, research on user modeling (which originated in the field of natural-language dialog systems) has spread into many disciplines whi...

Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.

13.44> . Drew Kessler for help with the SVE toolkit . The Virtual Environments group at Georgia Tech . The numerous experimental subjects who volunteered their time . Dawn Bowman iv TABLE OF CONTENTS Introduction ..................................................................... ................. 1 1.1 Motivation ..................................................................... ...............1 1.2 Definitions.......................................................... ..........................4 1.3 Problem Statement............................................................ ...............6 1.4 Scope of the Research............................................................. ..........7 1.5 Hypotheses........................................................... ........................8 1.6 Contributions........................................................ .....

... this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -- the Adaptive Place Advisor -- treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

KNOME is the user modeling component of UC, a natural language consultation system for the UNIX operating system. During the course of an interactive session with a user, KNOME infers the user's level of expertise from the dialog and maintains a model of the user's knowledge of the UNIX domain. KNOME's model of the user makes use of a double-stereotype system in which one set of stereotypes represents the user's expertise and another represents the difficulty level of the information. KNOME is used in UC to help: disambiguate the user's statements, avoid telling the user something that the user already knows, take advantage of prior user knowledge in presenting new information, and detect situations where the user lacks pertinent facts or where the user has a misconception. UC also models its own knowledge of UNIX with metaknowledge (explicit facts about the limitations of the system's own knowledge base), which is used to help in correcting user misconceptions. March 31,...

With the recent expansion of the Internet, the interest towards electronic sales has quickly grown and many tools have been built to help vendors to set up their Web stores. These tools offer all the facilities for building the store databases and managing the order processing and secure payment transactions, but they typically do not focus on issues like the personalization of the interaction with the customers. However, Web surfers are generally heterogeneous and have different needs and preferences; moreover, the trend of marketing strategies is to pay more and more attention to the specific buyers. So, the importance of personalizing the interaction with the user and the product presentation is increasing. In this paper, we describe the architecture of a configurable virtual Web store supporting personalized hypertextual interactions with users. Our system builds a user profile by applying user modeling techniques and stereotypical information about the characteristics of customer groups; this profile is used during the interaction in order to tailor the product descriptions and the selection of items to recommend to the user's needs, varying the layout of the hypertextual pages and the detail of the descriptions accordingly. Tailoring the system's behavior requires the parallel execution of several complex tasks during the interaction (e.g. identifying the user's preferences, selecting the products most suited to her, dynamically generating the hypertextual pages). Therefore, we have defined a multiagent architecture where these tasks

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 ofthe 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.

A wide-spread technique for user model acquisition is the use of acquisition heuristics, which are normally employed for inferring assumptions about the user's beliefs or goals from observed user actions. These beliefs or goals can often be characterized as presuppositions to communicative actions that the user performs. In the area of natural-language systems, presupposition analysis techniques have been applied for making assumptions about the dialogue partner based on the types of speech acts that he or she employs. In this paper, we will generalize this approach and investigate the analysis of so-called `dialogue acts', i.e. communicative actions on the user interface whose execution entails user beliefs or goals as presuppositions of the action. Dialogue acts

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We present a framework for the analysis and evaluation of travel, or viewpoint motion control, techniques for use in immersive virtual environments (VEs). The basic construct of this framework is a taxonomy of travel techniques, and we present a summary of three experiments mapping parts of the taxonomy to various performance measures. Since these initial experiments, we have expanded the framework to allow evaluation of not only the effects of different travel techniques, but also the effects of many outside factors simultaneously. Combining this expanded framework with the measurement of multiple response variables epitomizes the philosophy of testbed evaluation. This experimental philosophy leads to a deeper understanding of the interaction and the technique(s) in question, as well as to broadly generalizable results. We also present an example experiment within this expanded framework, which evaluates the users ability to gather information while traveling through a virtual environ...