Using dynamic user models in the recognition of the plans of the user

1996 ◽  
Vol 5 (2) ◽  
pp. 157-190 ◽  
Author(s):  
Liliana Ardissono ◽  
Dario Sestero
Keyword(s):  
User Models

UMAP 2020 Workshop on Explainable User Models and Personalised Systems (ExUM) Chairs' Welcome & Organization

2020 ◽  
Author(s):  
Cataldo Musto ◽  
Nava Tintarev ◽  
Oana Inel ◽  
Marco Polignano ◽  
Giovanni Semeraro ◽  
...  
Keyword(s):  
User Models

Virtual user models for the elderly and disabled for automatic simulated accessibility and ergonomy evaluation of designs

2012 ◽  
Vol 12 (4) ◽  
pp. 403-425 ◽  
Author(s):  
Nikolaos Kaklanis ◽  
Panagiotis Moschonas ◽  
Konstantinos Moustakas ◽  
Dimitrios Tzovaras
Keyword(s):  
The Elderly ◽  
User Models

Instrumented Interface Construction Evaluator

1996 ◽  
Vol 40 (17) ◽  
pp. 863-863
Author(s):  
James H. Hicinbothom
Keyword(s):  
Intelligent Agents ◽  
Cognitive Task ◽  
Human Factors Engineering ◽  
User Models ◽  
Engineering Research ◽  
Window System ◽  
Development Tools ◽  
System User ◽  
Computer Based ◽  
User Actions

Human factors engineering research into human-computer interaction (HCI) needs means to easily and objectively study HCI. The Instrumented Interface Construction (IICON) Evaluator, and its associated IICON Data Taps, provide such means. Any X Window system user interface built using development tools for which appropriate IICON Data Taps exist (e.g., IICON Data Taps/TAE+5.2) can be automatically instrumented. Instrumented interfaces can then be used by subjects, recording a complete transcript of user actions. Once an operator's session has been recorded, it can be replayed on-screen as needed for further observation and analysis. These capabilities are extremely helpful for developing cognitive task models (i.e., user models) for analytic purposes, or for construction of Intelligent Agents to be embedded in the operator's computer-based workstation. Furthermore, with a complete objective record of all operator actions, IICON Evaluator supports a wide variety of research investigations of HCI.

MyrrorBot : A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services

2021 ◽  
Vol 39 (4) ◽  
pp. 1-34
Author(s):  
Cataldo Musto ◽  
Fedelucio Narducci ◽  
Marco Polignano ◽  
Marco De Gemmis ◽  
Pasquale Lops ◽  
...  
Keyword(s):  
Natural Language ◽  
Information Needs ◽  
Effective Interaction ◽  
Personal Digital Assistant ◽  
User Profiling ◽  
Online Services ◽  
Intent Recognition ◽  
User Models ◽  
Time Required ◽  
The Way

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert? ) and personalized access to online services (e.g., Play a song I like ). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.

Can user models be learned at all? Inherent problems in machine learning for user modelling

2004 ◽  
Vol 19 (1) ◽  
pp. 61-88 ◽  
Author(s):  
MARTIN E. MÜLLER
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
User Model ◽  
User Modelling ◽  
Machine Learning Algorithms ◽  
Learning Problem ◽  
User Models ◽  
Modelling Techniques ◽  
Modelling Problem

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

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