Generic User Modeling Systems

By Alfred Kobsa. User modeling and user-adapted interaction, 2001

887 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0eafab63ad5bc238acdca604f4ea0545ee8621af

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

KnowledgeTree: a distributed architecture for adaptive e-learning (2004, background): “We think that the future student model servers should be based not on these standards, but on the earlier work on user model systems and servers, a popular research topic within the user modeling area [27].”
Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE (2003, methodology): “AEH systems reflect several learner characteristics to the design of the learner model, and apply this model to adapt various visible aspects of the system to individual learners (Brusilovsky, 1996; Kobsa, 2001).”
Gumo - The General User Model Ontology (2005, background): “Which groups of user dimensions can be identified? In [6] and [7] rough classifications for such categories can be found.”
Introduction to Recommender Systems Handbook (2011, background): “• Generic user models and cross domain recommender systems are able to mediate user data through different systems and application domains [41, 18, 52, 19, 20, 49, 15].”
Context-awareness on mobile devices - the hydrogen approach (2003, background): “Up to now such architectures are not trimmed to the special requirements of mobile devices regarding particularly the limitations of network connections, limited computing power and the characteristics of mobile users.”
Adaptive interfaces and agents (2002, background): “An informative example is TOOT (Litman & Pan, 2000), a prototype spoken dialog system for retrieving online train schedules.”
User Profiles for Personalized Information Access (2007, background): “In particular, Chapter 4 [40] discusses generic user modeling systems that are broader in scope, not necessarily focused on Internet applications.”
Sentiment analysis in Facebook and its application to e-learning (2014, background): “All the information about a user that will be considered with adaptation purposes must be stored in the user model and maintained up to date (Kobsa, 2007).”
Challenges and Solutions of Ubiquitous User Modeling (2012, methodology): “It was noted that such a stereotype could be used to reason across domains, for example, taking a model of the user’s preferences for books and using this to infer a model for the their preferences for movies.”