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Matthieu Jedor: Bandit algorithms for recommender system optimization
18 décembre 2020 @ 14:00 - 21 décembre 2020 @ 15:00
Résumé : In this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit. The task is modeled using the multi-armed bandit framework. In a first part, we look upon two problems that commonly occur in recommender systems: the large number of items to handle and the management of sponsored contents. In a second part, we investigate the empirical performance of bandit algorithms and especially how to tune a conventional algorithm in order to improve performance in stationary and non-stationary environments that arise in practice. This leads us to analyze both theoretically and empirically the greedy algorithm that, in some cases, outperforms the state-of-the-art.
Sous la direction de Vianney Perchet
Titre: Bandit algorithms for recommender system optimization