Rebounding Bandits for Modeling Satiation Effects
This work addresses the incremental improvement of recommender systems by incorporating psychological satiation dynamics, which is a domain-specific problem for AI and recommendation algorithms.
The paper tackles the problem of modeling satiation effects in recommender systems, where user enjoyment declines with repeated exposures, by introducing rebounding bandits as a multi-armed bandit setup with time-invariant linear dynamical systems, and proposes the Explore-Estimate-Plan algorithm to handle stochastic dynamics, achieving results that characterize optimal policies under deterministic conditions.
Psychological research shows that enjoyment of many goods is subject to satiation, with short-term satisfaction declining after repeated exposures to the same item. Nevertheless, proposed algorithms for powering recommender systems seldom model these dynamics, instead proceeding as though user preferences were fixed in time. In this work, we introduce rebounding bandits, a multi-armed bandit setup, where satiation dynamics are modeled as time-invariant linear dynamical systems. Expected rewards for each arm decline monotonically with consecutive exposures to it and rebound towards the initial reward whenever that arm is not pulled. Unlike classical bandit settings, methods for tackling rebounding bandits must plan ahead and model-based methods rely on estimating the parameters of the satiation dynamics. We characterize the planning problem, showing that the greedy policy is optimal when the arms exhibit identical deterministic dynamics. To address stochastic satiation dynamics with unknown parameters, we propose Explore-Estimate-Plan (EEP), an algorithm that pulls arms methodically, estimates the system dynamics, and then plans accordingly.