Enhancing News Recommendations with Deep Reinforcement Learning and Dynamic Action Masking
Abstract
A news recommender system is crucial for the transmission of news in new media. A deep reinforcement learning-based recommender system is suggested to integrate the characterization capabilities of neural networks with the strategic selection capabilities of reinforcement learning to enhance news recommendation efficacy. Dynamic action masks enhance the capacity to assess short-term interests of users. An optimized caching mechanism improves the efficiency of the experience cache, and a reward design characterized by region masking accelerates model training, thereby enhancing the performance of the recommender system for news recommendations. Experimental results indicate that the recommendation accuracy of the proposed model on the news dataset is on par with that of prevalent neural network recommendation techniques and surpasses existing state-of-the-art algorithms in ranking performance
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