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Journal articles on the topic 'User modelling'

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Published: 4 June 2021
Last updated: 29 January 2023

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1

Kass, Robert, and Tim Finin. "General User Modelling." ACM SIGCHI Bulletin 20, no. 1 (July 1988): 76. http://dx.doi.org/10.1145/49103.1046413.

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2

Daina, Nicolò. "Modelling the user variable." Nature Energy 3, no. 2 (February 2018): 88–89. http://dx.doi.org/10.1038/s41560-018-0091-6.

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3

Markopoulos, Panos. "Modelling User Tasks with the Unified Modelling Language." IFAC Proceedings Volumes 34, no. 16 (September 2001): 29–34. http://dx.doi.org/10.1016/s1474-6670(17)41497-2.

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4

Sosnovsky, Sergey, and Darina Dicheva. "Ontological technologies for user modelling." International Journal of Metadata, Semantics and Ontologies 5, no. 1 (2010): 32. http://dx.doi.org/10.1504/ijmso.2010.032649.

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5

Kay, J. "Reusable tools for user modelling." Artificial Intelligence Review 7, no. 3-4 (August 1993): 241–51. http://dx.doi.org/10.1007/bf00849557.

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6

Schütz, Jörg. "User modelling in text generation." Journal of Pragmatics 22, no. 6 (December 1994): 684–86. http://dx.doi.org/10.1016/0378-2166(94)90037-x.

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7

Cawsey, Alison. "User modelling in interactive explanations." User Modeling and User-Adapted Interaction 3, no. 3 (September 1993): 221–47. http://dx.doi.org/10.1007/bf01257890.

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8

Eberle, Peter, Christian Schwarzinger, and Christian Stary. "User modelling and cognitive user support: towards structured development." Universal Access in the Information Society 10, no. 3 (September 14, 2010): 275–93. http://dx.doi.org/10.1007/s10209-010-0210-z.

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9

Castillejo, Eduardo, Aitor Almeida, and Diego López-de-Ipiña. "Modelling users, context and devices for adaptive user interface systems." International Journal of Pervasive Computing and Communications 10, no. 1 (April 2014): 69–91. http://dx.doi.org/10.1108/ijpcc-09-2013-0028.

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10

Wang, Yanru. "An improved user interest modelling by considering user social relationship." International Journal of Collaborative Intelligence 2, no. 3 (2021): 191. http://dx.doi.org/10.1504/ijci.2021.122705.

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11

Wang, Yanru. "An improved user interest modelling by considering user social relationship." International Journal of Collaborative Intelligence 2, no. 3 (2021): 191. http://dx.doi.org/10.1504/ijci.2021.10047128.

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12

Savvopoulos, Anastasios, and Maria Virvou. "User modelling server for adaptive help." Intelligent Decision Technologies 5, no. 1 (January 5, 2011): 3–16. http://dx.doi.org/10.3233/idt-2011-0094.

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13

Montague, Kyle. "Accessible design through shared user modelling." ACM SIGACCESS Accessibility and Computing, no. 99 (January 2011): 37–42. http://dx.doi.org/10.1145/1948954.1948960.

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14

Ibeas, A., L. dell’Olio, M. Bordagaray, and J. de D. Ortúzar. "Modelling parking choices considering user heterogeneity." Transportation Research Part A: Policy and Practice 70 (December 2014): 41–49. http://dx.doi.org/10.1016/j.tra.2014.10.001.

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15

Preuschoff, Eva. "User-Oriented Modelling of Control Precess." IFAC Proceedings Volumes 31, no. 24 (September 1998): 27–30. http://dx.doi.org/10.1016/s1474-6670(17)38499-9.

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16

Luczak, Holger, Christopher Schlick, Alexander Kuenzer, and Frank Ohmann. "Syntactic user modelling with stochastic processes." Theoretical Issues in Ergonomics Science 2, no. 2 (January 2001): 97–123. http://dx.doi.org/10.1080/14639220110082065.

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17

Lizcano, David, Fernando Alonso, Javier Soriano, and Genoveva López. "Web-centred end-user component modelling." Future Generation Computer Systems 54 (January 2016): 16–40. http://dx.doi.org/10.1016/j.future.2015.07.002.

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18

Kim, Sehun, and Ki-Dong Lee. "Modelling user Mobility in Microcellular Systems." International Journal of Modelling and Simulation 21, no. 2 (January 2001): 132–37. http://dx.doi.org/10.1080/02286203.2001.11442195.

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19

De Bock, Yannick, Andres Auquilla, Ann Nowé, and Joost R. Duflou. "Nonparametric user activity modelling and prediction." User Modeling and User-Adapted Interaction 30, no. 5 (March 14, 2020): 803–31. http://dx.doi.org/10.1007/s11257-020-09259-3.

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20

MÜLLER, MARTIN E. "Can user models be learned at all? Inherent problems in machine learning for user modelling." Knowledge Engineering Review 19, no. 1 (March 2004): 61–88. http://dx.doi.org/10.1017/s0269888904000141.

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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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21

Zhou, Wei, David Heesom, Panagiotis Georgakis, and Joseph H.M. Tah. "User-centred design for collaborative 4D modelling." Construction Innovation 14, no. 4 (September 30, 2014): 493–517. http://dx.doi.org/10.1108/ci-01-2014-0008.

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Purpose – The purpose of this paper is to clarify the CSCW in collaborative 4D modelling and its user interface (UI)/interaction designs for prototyping. Four-dimensional (4D) modelling technology has potentials to integrate geographically dispersed planners to achieve collaborative construction planning. However, applying this technology in teamwork remains a challenge in computer-supported collaborative work (CSCW). Design/methodology/approach – The research adopted user-centred design (UCD) methodology to investigate a usable 4D collaboration prototype through analysis, design and usability testing. By applying CSCW theories, it first clarified the meaning of 4D CSCW to formulate design propositions as design target. By leveraging UCD theories, subsequently, the first-stage research sought an optimal standalone 4D modelling prototype following a parallel design approach. At the second stage, it further investigated into a collaborative 4D modelling prototype using an iterative design. It adopted collaborative task analysis into the UI/interaction design extension for a collaborative prototype based on results obtained from the first stage. The final usability testing was performed on the collaborative prototype to evaluate the designed CSCW and UI in a controlled geographically dispersed teamwork situation. Findings – The test results and user feedback verified their usability. It also disclosed design weaknesses in collaborators’ awareness and smooth tasks’ transitions for further enhancement. Originality/value – The combination of CSCW and UCD theories is practical for designing collaborative 4D modelling. It can also benefit designs for collaborative modelling in other dimensions like cost analysis, sustainable design, facility management, etc. in building information modelling.
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22

Case, Keith. "Tools for User-Centred Design." Advanced Engineering Forum 10 (December 2013): 28–33. http://dx.doi.org/10.4028/www.scientific.net/aef.10.28.

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User-Centred Design aims to involve users at all stages of the design of products. Some of the basic principles are briefly considered together with their relationship to ergonomics. Tools for the application of User-Centred Design are discussed including specific tools such as digital human modelling, personas, manikin characters, inclusive design and human behavioural modelling.
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23

Hale, Peter, Anthony E. Solomonides, and Ian Beeson. "User-driven modelling: Visualisation and systematic interaction for end-user programming." Journal of Visual Languages & Computing 23, no. 6 (December 2012): 354–79. http://dx.doi.org/10.1016/j.jvlc.2012.08.002.

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24

Darke, P., and G. Shanks. "User viewpoint modelling: understanding and representing user viewpoints during requirements definition." Information Systems Journal 7, no. 3 (July 1997): 213–19. http://dx.doi.org/10.1046/j.1365-2575.1997.d01-19.x.

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25

Nwana, Hyacinth S. "User modelling and user adapted interaction in an intelligent tutoring system." User Modeling and User-adapted Interaction 1, no. 1 (1991): 1–32. http://dx.doi.org/10.1007/bf00158950.

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26

Di Battista, Andrew, Christos Nicolaides, and Orestis Georgiou. "Modelling disease transmission from touchscreen user interfaces." Royal Society Open Science 8, no. 7 (July 2021): 210625. http://dx.doi.org/10.1098/rsos.210625.

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The extensive use of touchscreens for all manner of human–computer interactions has made them plausible instruments of touch-mediated disease transmission. To that end, we employ stochastic simulations to model human–fomite interaction with a distinct focus on touchscreen interfaces. The timings and frequency of interactions from within a closed population of infectious and susceptible individuals was modelled using a queuing network. A pseudo-reproductive number R was used to compare outcomes under various parameter conditions. We then apply the simulation to a specific real-world scenario; namely that of airport self-check-in and baggage drop. A counterintuitive result was that R decreased with increased touch rates required for touchscreen interaction. Additionally, as one of few parameters to be controlled, the rate of cleaning/disinfecting screens plays an essential role in mitigating R , though alternative technological strategies could prove more effective. The simulation model developed provides a foundation for future advances in more sophisticated fomite disease-transmission modelling.
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27

Alonso, Borja, Rosa Barreda, Luigi dell’Olio, and Angel Ibeas. "Modelling user perception of taxi service quality." Transport Policy 63 (April 2018): 157–64. http://dx.doi.org/10.1016/j.tranpol.2017.12.011.

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28

dell’Olio, Luigi, Angel Ibeas, and Patricia Cecín. "Modelling user perception of bus transit quality." Transport Policy 17, no. 6 (November 2010): 388–97. http://dx.doi.org/10.1016/j.tranpol.2010.04.006.

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29

Lowry, Gordon. "Modelling user acceptance of building management systems." Automation in Construction 11, no. 6 (October 2002): 695–705. http://dx.doi.org/10.1016/s0926-5805(02)00010-9.

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30

Sleeman, D. "UMFE: A user modelling front-end subsystem." International Journal of Man-Machine Studies 23, no. 1 (July 1985): 71–88. http://dx.doi.org/10.1016/s0020-7373(85)80025-0.

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31

Jameson, Anthony. "Modelling both the Context and the User." Personal and Ubiquitous Computing 5, no. 1 (February 28, 2001): 29–33. http://dx.doi.org/10.1007/s007790170025.

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32

Jokinen, Kristiina. "Adaptation and user expertise modelling in AthosMail." Universal Access in the Information Society 4, no. 4 (February 17, 2006): 374–92. http://dx.doi.org/10.1007/s10209-005-0002-z.

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33

Lockemann, Peter C., Guido Moerkotte, Andrea Neufeld, Klaus Radermacher, and Norbert Runge. "Database design with user-definable modelling concepts." Data & Knowledge Engineering 10, no. 3 (July 1993): 229–57. http://dx.doi.org/10.1016/0169-023x(93)90031-j.

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34

Roe, Peter J. "User modelling in CALL: Some fundamental issues." Computers & Education 23, no. 1-2 (August 1994): 27–33. http://dx.doi.org/10.1016/0360-1315(94)90029-9.

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35

Nyongesa, H. O., and S. Maleki-dizaji. "User modelling using evolutionary interactive reinforcement learning." Information Retrieval 9, no. 3 (June 2006): 343–55. http://dx.doi.org/10.1007/s10791-006-4536-3.

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36

Webb, Stephen A. "Modelling Service User Participation in Social Care." Journal of Social Work 8, no. 3 (July 2008): 269–90. http://dx.doi.org/10.1177/1468017808091040.

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37

Khalifa, Mohamed. "Modelling Ease of Learning of User Interfaces." Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 12, no. 3 (April 8, 2009): 250–67. http://dx.doi.org/10.1111/j.1936-4490.1995.tb00088.x.

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38

Wagstaff, Kiri L., Marie desJardins, and Eric Eaton. "Modelling and learning user preferences over sets." Journal of Experimental & Theoretical Artificial Intelligence 22, no. 3 (September 2010): 237–68. http://dx.doi.org/10.1080/09528130903119336.

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39

Kay, Judy. "The um toolkit for cooperative user modelling." User Modelling and User-Adapted Interaction 4, no. 3 (1995): 149–96. http://dx.doi.org/10.1007/bf01100243.

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40

Durugbo, Christopher. "Modelling user participation in organisations as networks." Expert Systems with Applications 39, no. 10 (August 2012): 9230–45. http://dx.doi.org/10.1016/j.eswa.2012.02.082.

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41

Bordagaray, Maria, Luigi dell'Olio, Angel Ibeas, and Patricia Cecín. "Modelling user perception of bus transit quality considering user and service heterogeneity." Transportmetrica A: Transport Science 10, no. 8 (November 15, 2013): 705–21. http://dx.doi.org/10.1080/23249935.2013.823579.

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42

Kok, André J. "A review and synthesis of user modelling in intelligent systems." Knowledge Engineering Review 6, no. 1 (March 1991): 21–47. http://dx.doi.org/10.1017/s0269888900005567.

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AbstractThis paper gives a state-of-the-art overview of the rapidly expanding field of user modelling in artificially intelligent systems. After showing how a user modelling component can improve parts of the processing of, and the interaction with, a large number of systems, the current situation in this field is sketched by means of a number of short descriptions of systems employing user modelling techniques. Next, a synthesis of this review is made by discussing the aspects on which the existing methods differ. It turns out that these aspects can best be approached from two points of view: the technical level and a more abstract, functional level. This dichotomy results in eight dimensions on which to compare the modelling methods. The synthesis is then completed by describing the existing modelling techniques, and classifying the reviewed systems. In the final section, current trends in the field are outlined and prerequisites for acceptance of user modelling on a larger scale are discussed.
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43

Barbaro, Eduardo, Eoin Martino Grua, Ivano Malavolta, Mirjana Stercevic, Esther Weusthof, and Jeroen van den Hoven. "Modelling and predicting User Engagement in mobile applications." Data Science 3, no. 2 (November 11, 2020): 61–77. http://dx.doi.org/10.3233/ds-190027.

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The mobile ecosystem is dramatically growing towards an unprecedented scale, with an extremely crowded market and fierce competition among app developers. Today, keeping users engaged with a mobile app is key for its success since users can remain active consumers of services and/or producers of new contents. However, users may abandon a mobile app at any time due to various reasons, e.g., the success of competing apps, decrease of interest in the provided services, etc. In this context, predicting when a user may get disengaged from an app is an invaluable resource for developers, creating the opportunity to apply intervention strategies aiming at recovering from disengagement (e.g., sending push notifications with new contents).In this study, we aim at providing evidence that predicting when mobile app users get disengaged is possible with a good level of accuracy. Specifically, we propose, apply, and evaluate a framework to model and predict User Engagement (UE) in mobile applications via different numerical models. The proposed framework is composed of an optimized agglomerative hierarchical clustering model coupled to (i) a Cox proportional hazards, (ii) a negative binomial, (iii) a random forest, and (iv) a boosted-tree model. The proposed framework is empirically validated by means of a year-long observational dataset collected from a real deployment of a waste recycling app. Our results show that in this context the optimized clustering model classifies users adequately and improves UE predictability for all numerical models. Also, the highest levels of prediction accuracy and robustness are obtained by applying either the random forest classifier or the boosted-tree algorithm.
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44

Glanznig, Michael. "User Experience Research: Modelling and Describing the Subjective." Interdisciplinary Description of Complex Systems 10, no. 3 (2012): 235–47. http://dx.doi.org/10.7906/indecs.10.3.3.

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45

Santos, Eugene, Hien Nguyen, Qunhua Zhao, and Hua Wang. "User Modelling for Intent Prediction in Information Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 47, no. 8 (October 2003): 1034–38. http://dx.doi.org/10.1177/154193120304700818.

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46

Borghol, Youmna, Siddharth Mitra, Sebastien Ardon, Niklas Carlsson, Derek Eager, and Anirban Mahanti. "Characterizing and modelling popularity of user-generated videos." Performance Evaluation 68, no. 11 (November 2011): 1037–55. http://dx.doi.org/10.1016/j.peva.2011.07.008.

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47

Gribaudo, Marco, Daniele Manini, and Carla Fabiana Chiasserini. "Modelling user radio access in dense heterogeneous networks." Performance Evaluation 146 (March 2021): 102167. http://dx.doi.org/10.1016/j.peva.2020.102167.

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48

Li, Daifeng, Andrew Madden, Chaochun Liu, Ying Ding, Liwei Qian, and Enguo Zhou. "Modelling online user behavior for medical knowledge learning." Industrial Management & Data Systems 118, no. 4 (May 14, 2018): 889–911. http://dx.doi.org/10.1108/imds-07-2017-0309.

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Purpose Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues. Design/methodology/approach To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords. Findings Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services. Practical implications The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences. Originality/value The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.
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49

Aro, Helena, and Teemu Pennanen. "A user-friendly approach to stochastic mortality modelling." European Actuarial Journal 1, S2 (June 28, 2011): 151–67. http://dx.doi.org/10.1007/s13385-011-0030-4.

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50

Raich, U. "Beam modelling with a window-oriented user interface." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 293, no. 1-2 (August 1990): 450–55. http://dx.doi.org/10.1016/0168-9002(90)91480-y.

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