In this work, we study sequential choice bandits with feedback. We propose bandit algorithms for a platform that personalizes users experience to maximize its rewards. For each action directed to a given user, the platform is given a positive reward, which is a non-decreasing function of the action, if this action is below the users threshold. Users are equipped with a patience budget, and actions that are above the threshold decrease the users patience. When all patience is lost, the user abandons the platform. The platform attempts to learn the thresholds of the users in order to maximize its rewards, based on two different feedback models describing the information pattern available to the platform at each action. We define a notion of regret by determining the best action to be taken when the platform knows that the users threshold is in a given interval. We then propose bandit algorithms for the two feedback models and show that upper and lower bounds on the regret are of the order of $tilde{O}(N^{2/3})$ and $tildeOmega(N^{2/3})$, respectively, where $N$ is the total number of users. Finally, we show that the waiting time of any user before receiving a personalized experience is uniform in $N$.
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