A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.