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User Modeling and User-Adapted Interaction 11

Editors:Alfred Kobsa
Dates:2001
Volume:11
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:16
Links:link.springer.com | Table of Contents
  1. UMUAI 2001 Volume 11 Issue 1/2
  2. UMUAI 2001-08 Volume 11 Issue 3
  3. UMUAI 2001-11 Volume 11 Issue 4

UMUAI 2001 Volume 11 Issue 1/2

Preface BIBFull-Text 1-4
  Alfred Kobsa
Predictive Statistical Models for User Modeling BIBAKFull-Text 5-18
  Ingrid Zukerman; David W. Albrecht
The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.
Keywords: content-based learning; collaborative learning; linear models; TFIDF-based models; Markov models; Neural networks; classifications; rule induction; Bayesian networks
Machine Learning for User Modeling BIBAKFull-Text 19-29
  Geoffrey I. Webb; Michael J. Pazzani
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.
Keywords: user modeling; machine learning; concept drift; computational complexity; World Wide Web; information agents
Techniques for Plan Recognition BIBAKFull-Text 31-48
  Sandra Carberry
Knowing a user's plans and goals can significantly improve the effectiveness of an interactive system. However, recognizing such goals and the user's intended plan for achieving them is not an easy task. Although much research has dealt with representing the knowledge necessary for plan inference and developing strategies that hypothesize the user's evolving plans, a number of serious problems still impede the use of plan recognition in large-scale, real-world applications. This paper describes the various approaches that have been taken to plan inference, along with techniques for dealing with ambiguity, robustness, and representation of requisite domain knowledge, and discusses areas for further research.
Keywords: plan inference; goals; plans; intentions
Generic User Modeling Systems BIBAKFull-Text 49-63
  Alfred Kobsa
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.
Keywords: user models; tool systems; user model shells; user model servers; user model agents
User Modeling in Human-Computer Interaction BIBAKFull-Text 65-86
  Gerhard Fischer
A fundamental objective of human-computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the "right" thing at the "right" time in the "right" way. Designers of collaborative human-computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time). User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.
Keywords: user modeling; human computer interaction; collaborative human-computer systems; high functionality applications; adaptive and adaptable systems; active help systems; critiquing systems; design environments
Adaptive Hypermedia BIBAKFull-Text 87-110
  Peter Brusilovsky
Adaptive hypermedia is a relatively new direction of research on the crossroads of hypermedia and user modeling. Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. The goal of this paper is to present the state of the art in adaptive hypermedia at the eve of the year 2000, and to highlight some prospects for the future. This paper attempts to serve both the newcomers and the experts in the area of adaptive hypermedia by building on an earlier comprehensive review (Brusilovsky, 1996; Brusilovsky, 1998).
Keywords: hypertext; hypermedia; user model; user profile; adaptive presentation; adaptive navigation support; Web-based systems; adaptation
Learner Control BIBAKFull-Text 111-127
  Judy Kay
This paper describes major trends in learner-adapted teaching systems towards greater learner control over the learning process. In the early teaching systems, the goal was to build a clever teacher able to communicate knowledge to the individual student. Recent and emerging work focuses on the learner exploring, designing, constructing, making sense and using adaptive systems as tools. Correspondingly, systems are being built to give the learner greater responsibility and control over all aspects of the learning, and especially over the learner model which is at the core of user-adaptation. A parallel trend is the growing acknowledgement of the importance of the learner's social context. Systems are increasingly being designed for learners working in groups of real or simulated peers. This paper discusses several elements of the shift to greater learner control, with a focus on the implications for learner modelling. The computer may offer the learner a choice of learning tools and companion learners, on-demand learning of various types, control over the elements of the systems and the possibility of controlling the amount of control. Learner control offers promising possibilities for improved learning. At the same time, there are pragmatic issues for achieving the benefits. The paper discusses three of these: the need to evaluate the effectiveness of the emergent learner-controlled systems; problems with learner control; and the need for interoperable and reusable components.
Keywords: learner model; student model; ITS; CSCL
Natural Language Processing and User Modeling: Synergies and Limitations BIBAKFull-Text 129-158
  Ingrid Zukerman; Diane Litman
The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.
Keywords: natural language generation; natural language understanding; plan recognition; surface features; dialogue systems
Adaptive Techniques for Universal Access BIBAKFull-Text 159-179
  Constantine Stephanidis
This paper discusses the contribution of adaptive techniques to Universal Access in Human-Computer Interaction. To this effect, the paper revisits the concept of Universal Access, briefly reviews relevant work on adaptive techniques, and follows their application in efforts to provide accessibility of interactive systems, from the early, product- and environment-level adaptation-based approaches, to more generic solutions oriented towards Universal Access. Finally, the paper highlights some of the research challenges ahead. The normative perspective of the paper is that adaptive techniques in the context of Universal Access have the potential to facilitate both accessibility and high quality interaction, for the broadest possible end-user population. This implies the design of systems that undertake context-sensitive processing so as to manifest their functional core in alternative interactive embodiments suitable for different users, usage patterns and contexts of use. Such a capability needs to be built into the system from the early phases of conception and design, and subsequently validated throughout its life cycle.
Keywords: universal design in HCI; universal access; adaptive techniques; adaptability; adaptivity; User Interfaces for All; Unified User Interfaces; AVANTI browser
Empirical Evaluation of User Models and User-Adapted Systems BIBAKFull-Text 181-194
  David N. Chin
Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insufficient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed.
Keywords: empirical evaluation; experimental design; covariant variables; effect size; treatment magnitude; power; sensitivity

UMUAI 2001-08 Volume 11 Issue 3

Information Filtering: Overview of Issues, Research and Systems BIBAKFull-Text 203-259
  Uri Hanani; Bracha Shapira; Peretz Shoval
An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more. The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science. Various systems cover different scope, have divergent functionality, and various platforms. There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques that are used to implement them. It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems. In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories.
Keywords: evaluation methods; information filtering; information retrieval; learning; measurement; user modeling; user profile

Book Review

M. Kyng and L. Mathiassen (eds.), Computers and Design in Context. BIBFull-Text 261-266
  Carol Strohecker

UMUAI 2001-11 Volume 11 Issue 4

Preface: Towards Adaptation of Interaction to Affective Factors BIBFull-Text 267-278
  Fiorella de Rosis
How Convincing is Mr. Data's Smile: Affective Expressions of Machines BIBAKFull-Text 279-295
  Christoph Bartneck
Emotions should play an important role in the design of interfaces because people interact with machines as if they were social actors. This paper presents a literature review on affective expressions through speech, music and body language. It summarizes the quality and quantity of their parameters and successful examples of synthesis. Moreover, a model for the convincingness of affective expressions, based on Fogg and Hsiang Tseng (1999), was developed and tested. The empirical data did not support the original model and therefore this paper proposes a new model, which is based on appropriateness and intensity of the expressions. Furthermore, the experiment investigated if the type of emotion (happiness, sadness, anger, surprise, fear and disgust), knowledge about the source (human or machine), the level of abstraction (natural face, computer rendered face and matrix face) and medium of presentation (visual, audio/visual, audio) of an affective expression influences its convincingness and distinctness. Only the type of emotion and multimedia presentations had an effect on convincingness. The distinctness of an expression depends on the abstraction and the media through which it is presented.
Keywords: abstraction; affective expressions; convincingness; distinctness; emotion; face; modality; music; speech
Modeling Emotion and Attitude in Speech by Means of Perceptually Based Parameter Values BIBAKFull-Text 297-326
  Sylvie J. L. Mozziconacci
This study focuses on the perception of emotion and attitude in speech. The ability to identify vocal expressions of emotion and/or attitude in speech material was investigated. Systematic perception experiments were carried out to determine optimal values for the acoustic parameters: pitch level, pitch range and speech rate. Speech was manipulated by varying these parameters around the values found in a selected subset of the speech material which consisted of two sentences spoken by a male speaker expressing seven emotions or attitudes: neutrality, joy, boredom, anger, sadness, fear, and indignation. Listening tests were carried out with this speech material, and optimal values for pitch level, pitch range, and speech rate were derived for the generation of speech expressing emotion or attitude, from a neutral utterance. These values were perceptually tested in re-synthesized speech and in synthetic speech generated from LPC-coded diphones.
Keywords: attitude; emotion; experimental phonetics; expression; perception; prosody; speech; speech technology