Non-Stationary Delayed Bandits with Intermediate Observations
This addresses the problem of delayed feedback in dynamic settings for recommender systems, offering a novel approach to mitigate non-stationarity.
The paper tackles the challenge of learning in non-stationary environments with delayed feedback, such as in online recommender systems, by introducing a model that uses intermediate signals to stabilize learning and developing an algorithm with sublinear regret guarantees.
Online recommender systems often face long delays in receiving feedback, especially when optimizing for some long-term metrics. While mitigating the effects of delays in learning is well-understood in stationary environments, the problem becomes much more challenging when the environment changes. In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete. However, the arising issues can be addressed if intermediate signals are available without delay, such that given those signals, the long-term behavior of the system is stationary. To model this situation, we introduce the problem of stochastic, non-stationary, delayed bandits with intermediate observations. We develop a computationally efficient algorithm based on UCRL, and prove sublinear regret guarantees for its performance. Experimental results demonstrate that our method is able to learn in non-stationary delayed environments where existing methods fail.