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UMUAI Tables of Contents: 0102030405060708091011121314151617

User Modeling and User-Adapted Interaction 7

Editors:Alfred Kobsa
Dates:1997
Volume:7
Publisher:Springer
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Papers:14
Links:link.springer.com | Table of Contents
  1. UMUAI 1997 Volume 7 Issue 1
  2. UMUAI 1997 Volume 7 Issue 2
  3. UMUAI 1997-06 Volume 7 Issue 3
  4. UMUAI 1997-09 Volume 7 Issue 4

UMUAI 1997 Volume 7 Issue 1

A Model for Adapting Explanations to the User's Likely Inferences BIBAKFull-Text 1-55
  Helmut Horacek
In order to generate natural, high quality textual presentations in technical domains, good explanations must not only be adapted to the knowledge attributed to the intended audience, but they must also take into account the inferential capabilities of the addressees. In this paper, we present a model for anticipating contextually-motivated inferences addressees are likely to draw. This model is used to motivate choices in presenting or omitting individual pieces of information; it takes into account the addressees' domain expertise and expectations about logical consequences of purposefully presented information. Several kinds of empirical evidence are incorporated into a text planning process that aims at exploiting conversational implicature, so that a most suitable portion of the plan can be selected for being uttered explicitly. This way, our method adds to discourse planners based on Rhetorical Structure Theory (RST) the ability to omit easily inferable information. Thus, it overcomes one of the main shortcomings of RST. In the course of this process, rules anticipating user inferences are invoked to determine contextually justified derivability of information. In this manner, text variants can be composed on the basis of a text plan entailing annotations about the inferability of pieces of information. Moreover, pragmatically-motivated preference criteria can be used to choose among several plausible variants. The model is formulated in a reasonably domain-independent way, so that the rules expressing aspects of conversational implicature can be incorporated into typical RST-based text planners.
Keywords: explanation; inference; natural language generation; stereotype user model.

Workshop Report

ABIS-96: GI Workshop on Adaptivity and User Modeling in Interactive Software Systems BIBFull-Text 57-62
  Hans-Günter Lindner

Book Review

The Trouble with Computers: Usefulness, Usability, and Productivity, Thomas K. Landauer BIBFull-Text 63-64
  Michel Desmarais

UMUAI 1997 Volume 7 Issue 2

A Concurrent, Distributed Architecture for Diagnostic Reasoning BIBAKFull-Text 69-105
  Stefano A. Cerri; Vincenzo Loia
This paper demonstrates the feasibility of modeling concurrent diagnostic reasoning (CDR) by means of the computational model of actors. Actors have a value added on top of objects, because they include the properties of abstraction, modularity and reuse of objects but allow really concurrent and distributed architectures, in the sense that memory (the environment) is assumed not to be shared among actors. Whether concurrency really implies efficiency is still debated. We are more concerned here with the actor-based design of the diagnostic reasoning model. As a testimony of the feasibility of our proposal, a concrete, actor-based diagnostic program is presented as a module for an Intelligent Tutoring System in the domain of school algebra. CDR is obtained from the coordinated behaviour of actors which possess limited local knowledge and accomplish the global goal of diagnostic reasoning by interacting with each other. We examine how the 'traditional' approaches to student modeling, such as overlay and bug models, can be re-visited in a distributed perspective of computational actors and how the latter view outperforms the previous ones.
Keywords: actors; actor model; diagnostic reasoning; student modeling; concurrent object-oriented programming; ABCL/1 programming language
Plan Recognition and Evaluation for On-line Critiquing BIBAKFull-Text 107-140
  Abigail S. Gertner
The Traum-AID system is a tool for assisting physicians during the management of patients with severe injuries. Originally, Traum-AID was conceived as a rule-based expert system combined with a planner. After this architecture had been implemented, we began to face the issue of how Traum-AID could communicate its plans to physicians in order to influence their behavior and have a positive effect on patient outcome. This paper describes Trauma-TIQ -- the critiquing interface for Traum-AID -- which examines the actions the physician intends to carry out and produces a critique in response to those intentions. Trauma-TIQ's two main components are a plan recognizer that uses the context of the case to disambiguate plans, and a plan evaluator that identifies errors and calculates their significance in order to determine an appropriate response. Unlike previously developed reminder systems, Trauma-TIQ evaluates the physician's proposed plan and attempts to intervene before problems occur. And unlike previous critiquing systems, it is able to provide ongoing decision support during the planning and delivery of care. In the context of time-critical patient management it is, therefore, a more appropriate means of interaction.
Keywords: critiquing; plan recognition; plan evaluation; decision-support systems; medical informatics

UMUAI 1997-06 Volume 7 Issue 3

The State of the Art in Text Filtering BIBAKFull-Text 141-178
  Douglas W. Oard
This paper develops a conceptual framework for text filtering practice and research, and reviews present practice in the field. Text filtering is an information seeking process in which documents are selected from a dynamic text stream to satisfy a relatively stable and specific information need. A model of the information seeking process is introduced and specialized to define text filtering. The historical development of text filtering is then reviewed and case studies of recent work are used to highlight important design characteristics of modern text filtering systems. User modeling techniques drawn from information retrieval, recommender systems, machine learning and other fields are described. The paper concludes with observations on the present state of the art and implications for future research on text filtering.
Keywords: information filtering; text retrieval; social filtering; collaborative; content-based; Selective Dissemination of Information; current awareness; recommender systems
A Feature-based Approach to Recommending Selections based on Past Preferences BIBAKFull-Text 179-218
  Bhavani Raskutti; Anthony Beitz; Belinda Ward
The increasing availability of a large number of interactive multi-media information services means that users have a large and diverse collection of choices open to them. This diversity and choice may present navigation difficulties to users which can dissuade them from using such services. One method of assisting users to navigate through large collections is to use information filtering to extract only the information relevant to an end-user according to his/her long-term preferences. In this paper, we describe a mechanism to acquire a user's long-term preferences (user profile), and then show how the acquired profile may be used to recommend selections that may be of interest to the user. The profile is acquired on the basis of a user's habits using a Heuristic-Statistical approach, and is used to create selection indices which are then used during on-line interactions to recommend selections. Our mechanism has been incorporated into an experimental Video On Demand (VOD) service that is implemented using a client-server architecture. The profile acquisition component is incorporated into a VOD server on a multi-tasking machine, while the VOD user interface resides on a personal computer. Our mechanism for acquiring profiles and making recommendations has been quantitatively evaluated on the basis of data collected about movie preferences.
Keywords: information filtering; personalised recommendations; acquisition of individual user models; interactive services

Book Review

Wiederverwendung von Plä en in deduktiven Planungssystemen (Reuse of Plans in Deductive Planning Systems), Jana Köhler BIBFull-Text 219-222
  Douglas E. Appelt

UMUAI 1997-09 Volume 7 Issue 4

User Models and Filtering Agents for Improved Internet Information Retrieval BIBAKFull-Text 223-237
  Sima C. Newell
Over the past few years, the amount of electronic information available through the Internet has increased dramatically. Unfortunately, the search tools currently available for retrieving and filtering information in this space are not effective in balancing relevance and comprehensiveness. This paper analyzes the results of experiments in which HTML documents are searched with user models and software agents used as intermediaries to the search. Simple user models are first combined with search specifications (or 'User Needs'), to define an Enhanced User Need. Then Uniform Resource Agents are constructed to filter information based on the EUN parameters. The results of searches using different agents are then compared to those obtained through a comparable simple keyword search, and it is shown that a user searching a pool of existing agents can obtain better search results than by conducting a traditional keyword search. This work thus demonstrates that the use of user models and information filtering agents do improve search results and may be used to improve Internet information retrieval.
Keywords: Agent; Internet; information; user; model; retrieval; filter; Uniform Resource Agents
Information Filtering Using User's Context on Browsing in Hypertext BIBAKFull-Text 239-256
  T. Hirashima; K. Hachiya; A. Kashihara
Browsing is one of the most popular ways to gather information in database with hypertext structure. In order to support a user to browse, modeling of the user's interests is one of the most important issues. Although there are several promising methods to infer the interests from the user's browsing behavior, they assume that the interests are consistent during the browsing. However, the user's interests are often strongly dependent on the local context of the browsing. This paper describes a method to model the user's shifting interests from the browsing history. An information filtering method using the model of the interests has been implemented. We call it 'context-sensitive filtering'. The results of an experimental evaluation, by real users' browsing for an encyclopedia in CD-ROM format, are also reported.
Keywords: Information filtering; browsing; context-sensitive; hypertext; user model; interests
Proficiency-Adapted Information Browsing and Filtering in Hypermedia Educational Systems BIBAKFull-Text 257-277
  Licia Calvi; Paul De Bra
We present a framework for proficiency-adapted information browsing and filtering in educational hypermedia systems. In hyperdocuments, information is acquired by browsing through highly interconnected sets of information nodes. In order to find specific information, users follow links to nodes they judge to be relevant. In order to help users find relevant information and new learning material that match their levels of domain knowledge, we present a framework for adapting the information nodes, and the links leading to them, to the user's proficiency in the subject matter. Such a proficiency-adapted, user-centered educational environment is intended to enhance learning. We believe that learning in educational hypertext-based applications cannot be reduced to traversing a static information space. Navigating through any space, be it a physical or an information space, normally requires that users have a prior degree of proficiency in the domain knowledge. Learning is an evolving dynamic process through which users progress from a situation of unfamiliarity to one of mastery of a knowledge corpus. Therefore, we propose a model of proficiency-adapted learning and information browsing in which the presented choices (links and the textual context of links) are selected based on the user's knowledge state. Ultimately, such an adaptive course not only guides the learning process of the student, but it gradually transforms itself into a reference guide.
Keywords: User-adaptivity; adaptive hypermedia systems; intelligent tutoring; information browsing; information filtering; cognitive load; self-modifying hyperdocuments
Feature-based and Clique-based User Models for Movie Selection: A Comparative Study BIBAKFull-Text 279-304
  Joshua Alspector; Aleksander Koicz
The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an 'information filtering agent' which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available.
Keywords: User modeling; information filtering; collaborative filtering; feature extraction; neural networks; linear models; regression trees; bagging; CART

Workshop Report

Bis-97: Gi Workshop on Adaptivity and User Modeling in Interactive Software Systems BIBFull-Text 305-314
  Ralph Schäfer; Mathias Bauer

Book Review

Intelligent Scheduling, Monte Zweben, Mark S. Fox BIBFull-Text 315-318
  M. Sasikumar