Application And Optimization of Deep Reinforcement Learning in News Recommendation

Abstract

In the era of online news platforms, the task of delivering personalized news recommendations to users has emerged as a critical challenge. This study delves into the utilization of Deep Q-Networks (DQN) within news recommendation systems, with a specific focus on the integration of loss functions and gradient descent optimization techniques. This combined approach aims to enhance the precision of estimating Q-values, ultimately resulting in more accurate and personalized article suggestions for users. The architectural design of the model involves the pairing of DQN with loss functions and gradient descent optimization, tailored for the domain of news recommendation. To validate this innovative approach, a comprehensive series of experiments has been executed, systematically benchmarking it against the conventional DQN framework. The empirical findings unequivocally demonstrate the superiority of the DQN fused with loss functions and gradient descent optimization across multiple performance metrics. These metrics encompass essential aspects such as click-through rates, user engagement duration, and overall satisfaction scores, affirming the effectiveness of the proposed approach. Furthermore, an extensive review of pertinent literature pertaining to the application of DQN in the realm of news recommendation is presented, providing readers with valuable contextual insights and a broader perspective. In summation, this paper underscores the compelling efficacy and untapped potential inherent in the fusion of loss functions and gradient descent optimization within DQN-based news recommendation systems.