Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce

Understand

The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking.

• Previous works either directly rank items by an end to end model, or rank items by a score function that trades-off the point-wise score and the diversity between items.
• However, there are two main existing challenges that are not well studied: (1) the evaluation of the slate is hard due to the complex mutual influences between items of one slate; (2) even given the optimal evaluation, searching the optimal slate is challenging as the action space is exponentially large.
• In this paper, we present a novel Generator and Critic slate re-ranking approach, where the Critic evaluates the slate and the Generator ranks the items by the reinforcement learning approach.