Value Penalized Q-Learning for Recommender Systems

TL;DR

This paper introduces Value Penalized Q-learning (VPQ), an offline reinforcement learning algorithm designed to improve recommender systems by addressing distributional shift and uncertainty in large action spaces.

Contribution

The paper proposes VPQ, which penalizes unstable Q-values using uncertainty-aware weights, and integrates it with existing recommender system models to enhance offline RL performance.

Findings

VPQ improves recommendation quality in real-world datasets.

The method effectively mitigates distributional shift issues.

VPQ can be used as a plugin with existing recommender models.

Abstract

Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improving customers' long-term satisfaction. A key approach to this goal is offline RL, which aims to learn policies from logged data. However, the high-dimensional action space and the non-stationary dynamics in commercial RS intensify distributional shift issues, making it challenging to apply offline RL methods to RS. To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm. It penalizes the unstable Q-values in the regression target by uncertainty-aware weights, without the need to estimate the behavior policy, suitable for RS with a large number of items. We derive the penalty weights from the variances across an ensemble of Q-functions. To alleviate distributional shift issues at test time, we further introduce the critic framework to integrate the proposed method with classic RS models. Extensive experiments conducted on two real-world datasets show that the proposed method could serve as a gain plugin for existing RS models.