Learning automata: an introduction
Learning automata: an introduction
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Experience with a learning personal assistant
Communications of the ACM
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Selecting Examples for Partial Memory Learning
Machine Learning
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
User profiling in personal information agents: a survey
The Knowledge Engineering Review
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Gain-based selection of ambient media services in pervasive environments
Mobile Networks and Applications
A framework for human-centered provisioning of ambient media services
Multimedia Tools and Applications
Language detection and tracking in multilingual documents using weak estimators
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Interface agents personalizing Web-based tasks
Cognitive Systems Research
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Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user's interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning " capabilities of the estimator used. Therefore, resorting to strong estimators that converge with probability 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art.