A contextual-bandit algorithm for mobile context-aware recommender system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Exploration / exploitation trade-off in mobile context-aware recommender systems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Agent-based architecture for context-aware and personalized event recommendation
Expert Systems with Applications: An International Journal
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The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2)how to model the user's situation and its related interests? Tithe best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-å-greedy algorithm we propose, which combines the standard å-greedy algorithm and both content-based filtering and case-based reasoning techniques.