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User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.


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Patent
Anna Patterson1
26 Jul 2004
TL;DR: In this article, a method of personalizing a search of a document collection to a user comprises monitoring documents accessed by a user, identifying first phrases present in one or more of the accessed documents, identifying corresponding first related phrases related to the corresponding identified first phrase, selecting search results comprising documents responsive to the query, identifying by operation of a processor configured to manipulate data within a computer system, one OR more second phrases related with the query and that are present in a user model.
Abstract: A method of personalizing a search of a document collection to a user comprises monitoring documents accessed by a user, identifying first phrases present in one or more of the accessed documents, identifying one or more corresponding first related phrases related to the corresponding identified first phrase, receiving a query including one or more second phrases from the user, selecting search results comprising documents responsive to the query, identifying by operation of a processor configured to manipulate data within a computer system, one or more second phrases related to one or more second phrases of the query and that are present in a user model, weighting scores of corresponding search results according to the identified one or more second related phrases, ranking the search results according to their weighted scores to provide personalized search results and presenting them to the user.

123 citations

Journal ArticleDOI
01 Jun 2010
TL;DR: This paper creates user profiles capturing the strength of users' behavioral patterns, which can be used to identify users, and indicates that these profiles can be more accurate at identifying users than decision trees when sufficient web activities are observed.
Abstract: In this paper, we propose a simple, yet powerful approach to profile users' web browsing behavior for the purpose of user identification. The importance of being able to identify users can be significant given a wide variety of applications in electronic commerce, such as product recommendation, personalized advertising, etc. We create user profiles capturing the strength of users' behavioral patterns, which can be used to identify users. Our experiments indicate that these profiles can be more accurate at identifying users than decision trees when sufficient web activities are observed, and can achieve higher efficiency than Support Vector Machines. The comparisons demonstrate that profile-based methods for user identification provide a viable and simple alternative to this problem.

123 citations

Journal ArticleDOI
TL;DR: A standardized terminology of the game elements used in tailored gamification, the discussion on the most suitable game elements for each users’ characteristic, and a research agenda including dynamic modeling, exploring multiple characteristics simultaneously, and understanding the effects of other aspects of the interaction on user experience are outlined.
Abstract: Gamification is increasingly becoming a pertinent aspect of any UI and UX design. However, a canonical dearth in research and application of gamification has been related to the role of individual differences in susceptibility to gamification and its varied designs. To address this gap, this study reviews the extant corpus of research on tailored gamification (42 studies). The findings of the review indicate that most studies on the field are mostly focused on user modeling for a future personalization, adaptation, or recommendation of game elements. This user model usually contains the users’ preferences of play (i.e., player types), and is mostly applied in educational settings. The main contributions of this paper are a standardized terminology of the game elements used in tailored gamification, the discussion on the most suitable game elements for each users’ characteristic, and a research agenda including dynamic modeling, exploring multiple characteristics simultaneously, and understanding the effects of other aspects of the interaction on user experience.

123 citations

Book ChapterDOI
01 Jan 1989
TL;DR: The general architecture of a domain independent system for building and maintaining long term models of individual users, and a prototype general user modeling shell that is implemented in Prolog is described.
Abstract: This chapter discusses the application of various kinds of default reasoning in system maintained models of users. In particular, we describe the general architecture of a domain independent system for building and maintaining long term models of individual users.The user modeling system is intended to provide a well defined set of services for an application system interacting with various users, and must build and maintain models of them. As the application system interacts with a user, it can acquire knowledge of him, and pass that knowledge on to the user model maintenance system for incorporation. We describe a prototype general user modeling shell (hereafter called GUMS) that we have implemented in Prolog. This system possesses some of the desirable characteristics we discuss.

123 citations

Patent
30 Mar 2006
TL;DR: In this article, an architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy.
Abstract: An architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy. User model predictions predict what goal or action speech application users are likely to pursue based in part on past user behavior. Speech recognition results indicate what goal speech application users are likely to have spoken based in part on words spoken under specific constraints. Discrepancies between the predictive data and the speech recognition data are identified and a dialog repair is engaged for repairing these discrepancies. By engaging in repairs when there is a discrepancy between the predictive results and the speech recognition results, and utilizing feedback obtained via interaction with a user, the architecture can learn about the reliability of both user model predictions and speech recognition results for future processing.

123 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202327
202269
2021150
2020167
2019194
2018216