2017 · 31 citations · 21 references
ComputingData ScienceInteraction DesignRecommender SystemsUser Experience DesignRecommendation SystemsUser Experience OptimizationUser ModelingAdaptive RecommendationExploitation Trade-offExploitation TradeoffExploration - Exploitation Trade-offExploration In RecommendationExploration Trade-offExploration-exploitation Trade-offInteractive Recommendation
Recommender Systems (RS) help users discover interesting products by means of relevant proactive suggestions. To accomplish this, RS must learn about user's unknown/unclear tastes, and constantly adapt to the dynamic nature of their environment. In this research, I focus on the notion that RS are faced with an optimization problem when generating recommendations: exploit the known user model, or explore other preferences the user might have. My PhD work aims to define the role of exploitation and exploration in RS, and proposes mechanisms that would allow to balance and control this trade-off. In this extended abstract I present the motivation, related work and research plan that guide the project.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks · 2005 · 25.8K citations
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford et al. · 2010 · 2.5K citations · Full text