Diversity-Preserving K-Armed Bandits, Revisited
This work addresses the challenge of maintaining diversity in recommendation systems, but it is incremental as it builds on an existing framework.
The paper tackles the problem of diversity-preserving recommendations in K-armed bandits by designing a UCB algorithm that achieves bounded distribution-dependent regret when diversity is desirable, with regret lower bounds showing a logarithmic regret otherwise.
We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction to the setting of linear bandits. We design a UCB algorithm using the specific structure of the setting and show that it enjoys a bounded distribution-dependent regret in the natural cases when the optimal mixed actions put some probability mass on all actions (i.e., when diversity is desirable). The regret lower bounds provided show that otherwise, at least when the model is mean-unbounded, a $\ln T$ regret is suffered. We also discuss an example beyond the special case of polytopes.