High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits
arXiv:2305.18305 · doi:10.14428/esann/2022.ES2022-79
Abstract
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.
7 pages, 7 figures, Esann 2022 conference