Ranking in Contextual Multi-Armed Bandits. (arXiv:2207.00109v1 [stat.ML])

We study a ranking problem in the contextual multi-armed bandit setting. A
learning agent selects an ordered list of items at each time step and observes
stochastic outcomes for each position. In online recommendation systems,
showing an ordered list of the most attractive items would not be the best
choice since both position and item dependencies result in a complicated reward
function. A very naive example is the lack of diversity when all the most
attractive items are from the …

arxiv ml multi-armed bandits ranking