Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A diary study of mobile information needs
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Exploitation and exploration in a performance based contextual advertising system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
WAINA '12 Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
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Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user's content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user's situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.