MaHRL

Abstract

As huge commercial value of the recommender system, there has been growing interest to improve its performance in recent years. The majority of existing methods have achieved great improvement on the metric of click, but perform poorly on the metric of conversion possibly due to its extremely sparse feedback signal. To track this challenge, we design a novel deep hierarchical reinforcement learning based recommendation framework to model consumers' hierarchical purchase interest. Specifically, the high-level agent catches long-term sparse conversion interest, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and catches short-term click interest via interacting with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel multi-goals abstraction based deep hierarchical reinforcement learning algorithm (MaHRL). Our proposed algorithm contains three contributions: 1) the high-level agent generates multiple goals to guide the low-level agent in different sub-periods, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder structure and its parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciated reward assignment mechanism is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.