A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System
- Cite this paper as:
- Bouneffouf D., Bouzeghoub A., Gançarski A.L. (2012) A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System. In: Huang T., Zeng Z., Li C., Leung C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg
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.
KeywordsRecommender system Machine learning Exploration/exploitation dilemma Artificial intelligence
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