Innovative Application of Reinforcement Learning in User Growth and Behavior Prediction

Abstract

With the rapid development of Internet technology, more and more industries realize the importance of user scale and user prediction. Although the traditional user prediction methods have achieved certain results in some specific scenarios, they generally have the disadvantages of inaccurate prediction and inadaptability to the changes of scenarios. In recent years, due to the characteristics of autonomous learning and strong adaptability, machine learning technology based on reinforcement learning has broad application prospects in personalized recommendation system, multi task optimization, user behavior prediction and so on. The focus of this paper is on the means and methods to help expand the scale of users and improve the ability of behavior prediction through reinforcement learning. This includes the establishment of personalized recommendation based on reinforcement learning; combining multi task learning with Multi-Agent Reinforcement Learning; a novel method combining deep reinforcement learning and behavior sequence prediction is studied. This paper analyzes the current situation of reinforcement learning in this field, and puts forward innovative strategies to further optimize the existing model, so as to better improve the real-time and adaptability. This paper provides a new idea for the application of reinforcement learning assisted behavior prediction, and also lays a theoretical foundation for future related work.