JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System

In the realm of online recommendation systems, the Combinatorial Recommender (CR) system stands out for its unique approach. It presents users with a list of items on a result page, where user behavior is simultaneously influenced by contextual information and the items listed. Formulated as a combinatorial optimization problem, the objective of the CR system is to maximize the recommendation reward across the entire list of items. Despite the significant potential of CR systems, developing a practical and efficient model remains substantial challenges. These challenges stem from the dynamic nature of online environments and the pressing need for personalized recommendations. To tackle these challenges, we decompose the overarching problem into two sub-problems: list generation and list evaluation. We propose novel and pragmatic model architectures for each sub-problem aiming to concurrently enhance both effectiveness and efficiency. To further adapt the CR system to online scenarios, we integrate a bootstrap algorithm into an actor-critic reinforcement framework. This innovative approach called JD Recommender System (JDRec) is designed to continuously refine the recommendation mode through sustained user interaction, ensuring the system's adaptability and relevance. The proposed JDRec framework, tested through rigorous offline and online experiments, has shown promising results. It has been successfully deployed in online JD recommendation systems, yielding a notable improvement in click-through rate by 2.6% and augmenting the total value of the platform by 5.03%. Besides, we release the large scale dataset used in our work to facilitate further research.