Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Personalized portals for the wireless user based on mobile agents
WMC '02 Proceedings of the 2nd international workshop on Mobile commerce
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
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
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
The Journal of Machine Learning Research
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
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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The contextual bandit problem has been studied in the recommender system community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles this problem by modeling the Mobile Context-Aware Recommender Systems (MCRS) as a contextual bandit algorithm and it is based on dynamic exploration/exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demonstrate that our algorithm outperforms surveyed algorithms.