future regret
paper
In this section we provide a detailed proof for the main theorem. First we state some facts about the learning rate and the algorithm. This bound contains three parts. The first is an upper bound for the first step when there is no data. The third part is an "average" of the estimated future regret.
High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits
We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of the art, simultaneously achieving both higher accuracy and lower regret.
2305.18305
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