Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation

Alexandre Letard, Nicolas Gutowski, Olivier Camp, Tassadit Amghar

Abstract: Highlights•Bandit literature lacks formal algorithm review, hindering clarity and comparability.•There is no silver bullet: no algorithm can be the best performer in every instance.•Recommender systems need to balance accuracy, diversity, multi-item recommendations.•Optimal algorithm balances criteria, matching decision maker’s preferred trade-off.•Dynamic selection ensures safe performance when optimal algorithm is unknown.