| Preface | | BIB | Full-Text | 1-4 | |
| Alfred Kobsa | |||
| Predictive Statistical Models for User Modeling | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | | BIBAK | Full-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 | |||
| Information Filtering: Overview of Issues, Research and Systems | | BIBAK | Full-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 | |||
| M. Kyng and L. Mathiassen (eds.), Computers and Design in Context. | | BIB | Full-Text | 261-266 | |
| Carol Strohecker | |||
| Preface: Towards Adaptation of Interaction to Affective Factors | | BIB | Full-Text | 267-278 | |
| Fiorella de Rosis | |||
| How Convincing is Mr. Data's Smile: Affective Expressions of Machines | | BIBAK | Full-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 | | BIBAK | Full-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 | |||