  We study the effect of persistence of engagement on learning in a stochastic
multi-armed bandit setting. In advertising and recommendation systems,
repetition effect includes a wear-in period, where the user's propensity to
reward the platform via a click or purchase depends on how frequently they see
the recommendation in the recent past. It also includes a counteracting
wear-out period, where the user's propensity to respond positively is dampened
if the recommendation was shown too many times recently. Priming effect can be
naturally modelled as a temporal constraint on the strategy space, since the
reward for the current action depends on historical actions taken by the
platform. We provide novel algorithms that achieves sublinear regret in time
and the relevant wear-in/wear-out parameters. The effect of priming on the
regret upper bound is also additive, and we get back a guarantee that matches
popular algorithms such as the UCB1 and Thompson sampling when there is no
priming effect. Our work complements recent work on modeling time varying
rewards, delays and corruptions in bandits, and extends the usage of rich
behavior models in sequential decision making settings.
