Modeling Attrition in Recommender Systems with Departing Bandits

arXiv:2203.13423v2 Announce Type: replace
Abstract: 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 …

abstract arxiv attrition cs.ir cs.lg modeling multi-armed bandits novel policy recommender systems setup stat.ml systems type work world