A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Abstract

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.