The Development of Behavior-Based User Models for a Computer System

Bushey, Robert; Mauney, Jennifer Mitchel; Deelman, Thomas

doi:10.1007/978-3-7091-2490-1_11

Cited by 14 publications

(11 citation statements)

References 4 publications

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“…These, in return, depend on the quality of information about the user population and the extent to which subgroups with different application-relevant characteristics can be empirically distinguished (see Bushey, 1999, for a methodology to gain such information).…”

Section: Figure 3 Domain-specific Acquisition Rule In Timsmentioning

confidence: 99%

Personalised hypermedia presentation techniques for improving online customer relationships

Kobsa

Koenemann²,

Pohl

2001

The Knowledge Engineering Review

408

298

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This article gives a comprehensive overview of techniques for personalised hypermedia presentation. It describes the data about the computer user, the computer usage and the physical environment that can be taken into account when adapting hypermedia pages to the needs of the current user. Methods for acquiring these data, for representing them as models in formal systems and for making generalisations and predictions about the user based thereon are discussed. Different types of hypermedia adaptation to the individual user's needs are distinguished and recommendations for further research and applications given. While the focus of the article is on hypermedia adaptation for improving customer relationship management utilising the World Wide Web, many of the techniques and distinctions also apply to other types of personalised hypermedia applications within and outside the World Wide Web, like adaptive educational systems.

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Section: Figure 3 Domain-specific Acquisition Rule In Timsmentioning

confidence: 99%

Personalised hypermedia presentation techniques for improving online customer relationships

Kobsa

Koenemann²,

Pohl

2001

The Knowledge Engineering Review

408

298

View full text Add to dashboard Cite

show abstract

“…For example, a WAP browser must restrict graphical content. There is little room for user likes and dislikes, although [14] describe a system which implements different interfaces for different users on desktop systems. Those who have more difficulty navigating through the system use a menu-based interface whereas those with a greater awareness of the system contents are given an interface using a number of shortcut keys.…”

Section: Introductionmentioning

confidence: 98%

Adaptive user modelling in intelligent telephone and email assistants

Martin

Azvine

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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With the burgeoning complexity and capabilities of modern information appliances and services, user modelling is becoming an increasingly important research area. Simple user profiles already personalise many software products and consumer goods such as digital TV recorders and mobile phones. A user model should be easy to initialise, and it must adapt in the light of interaction with the user. In many cases, a large amount of training data is needed to generate a user model, and adaptation is equivalent to retraining the system. This paper briefly outlines the user modelling problem and work done at BTexact on an intelligent personal assistant (IPA) which incorporates a user profile. We go on to describe FILUM, a more flexible method of user modelling, and show its application to the telephone assistant and email assistant components of the IPA, with tests to illustrate its usefulness.

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“…User modeling [2], [3] is a mature research field mostly involved in the information technology context. User modeling is mainly utilized in software systems for inferring the users' goals, skills, knowledge, needs and preferences and thus achieving more adequate adaptation and personalization on the basis of the user activity pattern built [4].…”

Section: Introductionmentioning

confidence: 99%

Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus

Xhafa

Caballé

Barolli

et al. 2010

2010 International Conference on Intelligent Networking and Collaborative Systems

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Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.

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The Development of Behavior-Based User Models for a Computer System

Cited by 14 publications

References 4 publications

Personalised hypermedia presentation techniques for improving online customer relationships

Personalised hypermedia presentation techniques for improving online customer relationships

Adaptive user modelling in intelligent telephone and email assistants

Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus

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