The Exploration-Exploitation Trade-off in Interactive Recommender Systems
Andrea Barraza‐Urbina · 2017
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.