Modeling Attrition in Recommender Systems with Departing Bandits
This addresses the issue of user retention in real-world recommender systems, though it is incremental as it extends existing bandit models to include policy-dependent horizons.
The paper tackles the problem of user attrition in recommender systems by introducing a multi-armed bandit setup where user departure depends on recommendation dissatisfaction, and it presents an efficient learning algorithm achieving $ ilde{O}(\sqrt{T})$ regret for a two-type user scenario.
Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. Our setup consists of a finite set of user types, and multiple arms with Bernoulli payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward probability. Each user's type is initially unknown and can only be inferred through their response to recommendations. Moreover, if a user is dissatisfied with their recommendation, they might depart the system. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types. While naive approaches cannot handle this setting, we provide an efficient learning algorithm that achieves $\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.