User modeling
Johnson, A., Taatgen, N.A.; id_orcid 0000-0001-7286-3460 · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2005
Cited by 0
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About 10,000 papers for “User modeling”
User Modeling
Ray Adams · Human factors and ergonomics · 2009
Cited by 1
User modeling
Johnson, A., Taatgen, N.A., Proctor, Robert W. et al. · Data Archiving and Networked Services (DANS) · 2005
Cited by 0
User modeling
Johnson, A., Taatgen, N.A.; id_orcid 0000-0001-7286-3460 · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2005
Cited by 0
User Modeling
Christian Bauer, Christian Bauer, Christian Bauer · 2001
Cited by 0
User Modeling
Peter A. Gloor · Birkhäuser Boston eBooks · 1997
This chapter briefly introduces user modeling, with an emphasis on its application to information retrieval. An accurate model of the user can considerably improve the success of an IR system session, as IR systems often lack the capability of adapting their behavior to different categories of users. Naive users, users with a specific background in the search domain, newcomers, and experienced users are all treated the same. The goals that should be reached by applying user modeling techniques to IR are threefold: 1. Linguistic gaps between the user and the system should be closed. 2. Users should be supported at the conceptual level in the analysis of their information needs, the formulation of an appropriate search strategy, and the evaluation of the results. 3. A user needs a customized preview of the information space in order to decide on meaningful queries. Cybermap applies user modeling techniques described in this chapter to provide such an overview.
Cited by 0
User modeling
Friedrich Schneider, Andreas Buehn, Claudio E. Montenegro · 1993
This paper presents estimations of the shadow economies for 162 countries, including developing, Eastern European, Central Asian, and high income OECD countries over 1999 to 2006/2007. According to our estimations, the weighted average size of the shadow economies (as a percentage of "official " gross domestic product) in Sub-Saharan Africa is 38.4 percent; in Europe and Central Asia (mostly transition countries) 36.5 percent, and in high-income OECD countries 13.5 percent.. We find that an increased burden of taxation (direct and indirect ones), combined with (labor market) regulations and the quality of public goods and services as well as the state of the “official ” economy are the driving forces of the shadow economy.
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User modeling
Yu Lu A, Mehul Motani B, Wai-choong Wong B · 2011
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
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User Modeling
Addie Johnson, Niels Taatgen, Updating User et al. · 2003
Agents as Team Members............................................................................................. 33 User Modeling Servers ................................................................................................. 35 Adapting to Physical Limitations .................................................................................. 37 Summary .......................................................................................................................... 37 References........................................................................................................................ 39 Table 25-1. Overview of Constraints that the Model Human Processor (MHP), Soar, EPIC and ACT-R Impose on Cognitive Processing...............................................................45 Table 25-2. Adaptable Aspects of Web Use.......................................................
Cited by 0
Modeling needs user modeling
Mustafa Mert Çelikok, Pierre-Alexandre Murena, Samuel Kaski · Frontiers in Artificial Intelligence · 2023
Modeling has actively tried to take the human out of the loop, originally for objectivity and recently also for automation. We argue that an unnecessary side effect has been that modeling workflows and machine learning pipelines have become restricted to only well-specified problems. Putting the humans back into the models would enable modeling a broader set of problems, through iterative modeling processes in which AI can offer collaborative assistance. However, this requires advances in how we scope our modeling problems, and in the user models. In this perspective article, we characterize the required user models and the challenges ahead for realizing this vision, which would enable new interactive modeling workflows, and human-centric or human-compatible machine learning pipelines.
Cited by 3Open access
From Classic User Modeling to Scrutable User Modeling
Giorgio Gianforme, Sergio Miranda, Francesco Orciuoli et al. · 2009
Cited by 4Open access
User Modeling and User Interfaces
Kathleen R. Mckeovm · 2014
In the past several years, it has become increasingly apparent that there is strong disagreement between researchers within and outside of AI on how to build systems for man-machine communication. Within AI, and specifically within natural language, researchers have noted that human speakers and hearers draw on their knowledge about each other when communicating. This knowledge is used both in understanding, and responding to, a speaker’s utterances. User models are a means for representing various types of information about speakers and hearers so that systems are able to reason about their users when interpreting input and producing responses. While there is disagreement in the AI community about the form of user model that should be used, there is an implicit assumption that some form of knowledge about
Cited by 0
Modeling user navigation
Sas, Corina, Reilly, R., O'Hare, G.M.P. et al. · Lancaster EPrints (Lancaster University) · 2003
This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed.
Cited by 0Open access
Cognitive user modeling
van Rijn, Hedderik, Johnson, A., Taatgen, Niels et al. · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2011
Cited by 0
Temporary User Modeling
For Adaptive Product, Tanja Joerding · 1999
This work proposes a temporary user modeling approach that enables the immediate adaptation of product presentations in the Web to the individual customer at runtime by using a machine learning algorithm. We focus on the question of how monitored user interactions can be preprocessed to get example data for the learning algorithm and how existing decision tree or decision list algorithms fit the requirements of the new application field of electronic shopping.
Cited by 0
Ubiquitous User Modeling
Dominikus Heckmann · Publications of the UdS (Saarland University) · 2005
More and more interactions take place between humans and mobile or connected IT-systems in daily life. This offers a great opportunity, especially to user modeling, to reach better adaptation with ongoing evaluation of user behavior. This work develops a complete framework to realize the newly defined concept of ubiquitous user modeling. The developed tools cover methods for the uniform exchange and the semantic integration of partial user models. They also account for the extended needs for privacy and the right of every human for introspection and control of their collected data. The SITUATIONALSTATEMENTS and the exchange language USERML have been developed on the syntactical level, while the general user model ontology GUMO and the UBISWORLD ontology have been developed on the semantical level. A multilevel conflict resolution method, which handles the problem of contradictory statements, has been implemented together with a web-based user model service, such that the road capability and the scalability can be proven with this approach.
Cited by 135Open access
Modeling User Knowledge
Pandit, Aabha, Owen, Heather, Romanowski, Alois · Figshare · 2024
<b>Modelling User Knowledge Dataset</b>This dataset contains students' knowledge status about the subject of Electrical DC Machines, where the users' knowledge class were classified using intuitive knowledge classifier.The dataset does not contain any missing values.<b>Dataset Variables:</b>STG: the degree of study time for goal object materialsSCG: the degree of repetition number of user for goal object materials STR: the degree of study time of user for related objects with goal objectLPR: the exam performance of user for related objects with goal objectPEG: the exam performance of user for goal objectsUNS: the knowledge level of userClass Labels: Very Low: 50 Low: 129 Middle: 122 High: 130<br>
Cited by 0Open access
User Modeling 2005
Lecture notes in computer science · 2005
Cited by 176
User Behavior Modeling
Reda Alhajj, Jon Rokne · 2018
Cited by 0
Modeling User Knowledge
Pandit, Aabha, Owen, Heather, Romanowski, Alois · UR Research (University of Rochester) · 2024
<b>Modelling User Knowledge Dataset</b>This dataset contains students' knowledge status about the subject of Electrical DC Machines, where the users' knowledge class were classified using intuitive knowledge classifier.The dataset does not contain any missing values.<b>Dataset Variables:</b>STG: the degree of study time for goal object materialsSCG: the degree of repetition number of user for goal object materials STR: the degree of study time of user for related objects with goal objectLPR: the exam performance of user for related objects with goal objectPEG: the exam performance of user for goal objectsUNS: the knowledge level of userClass Labels: Very Low: 50 Low: 129 Middle: 122 High: 130<br>
Cited by 0Open access