Reinforcement Re-ranking with 2D Grid-based Recommendation Panels

TL;DR

This paper introduces Panel-MDP, a reinforcement learning approach for effectively re-ranking items into 2D grid-based panels in recommender systems, addressing the challenge of non-sequential slot arrangements.

Contribution

The paper proposes a novel Markov decision process model for 2D grid-based recommendation re-ranking, improving user experience over traditional list-based methods.

Findings

Panel-MDP outperforms baseline models in grid panel recommendation quality.

Reinforcement learning with PPO effectively learns item placement strategies.

Simulation results demonstrate significant improvements in user engagement metrics.

Abstract

Modern recommender systems usually present items as a streaming, one-dimensional ranking list. Recently there is a trend in e-commerce that the recommended items are organized grid-based panels with two dimensions where users can view the items in both vertical and horizontal directions. Presenting items in grid-based result panels poses new challenges to recommender systems because existing models are all designed to output sequential lists while the slots in a grid-based panel have no explicit order. Directly converting the item rankings into grids (e.g., pre-defining an order on the slots) overlooks the user-specific behavioral patterns on grid-based panels and inevitably hurts the user experiences. To address this issue, we propose a novel Markov decision process (MDP) to place the items in 2D grid-based result panels at the final re-ranking stage of the recommender systems. The model, referred to as Panel-MDP, takes an initial item ranking from the early stages as the input. Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}. At each time step, Panel-MDP sequentially executes two sub-actions: first deciding whether the current item in the initial ranking list is preferred by the user; then selecting a slot for placing the item if preferred, or skipping the item otherwise. The process is continued until all of the panel slots are filled. The reinforcement learning algorithm of PPO is employed to implement and learn the parameters in the Panel-MDP. Simulation and experiments on a dataset collected from a widely-used e-commerce app demonstrated the superiority of Panel-MDP in terms of recommending 2D grid-based result panels.