A Machine Learning-based Approach for Predicting User Behavior in Online Systems
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Abstract
The integration of reinforcement learning (RL) in online systems has redefined the landscape of user interaction and engagement. This research delves into the application of RL for predicting and influencing user behavior in online systems, addressing the dynamic nature of user preferences and system dynamics. We propose a novel RL model designed to optimize user engagement, encourage desired user actions, and align with system objectives. The model prioritizes transparency and interpretability, essential for user trust and ethical AI use. Our work contributes to the field by emphasizing ethical considerations and offering insights into the model's decision-making processes. We present a comprehensive evaluation of the model's performance using a range of performance metrics, including Click-Through Rate (CTR), Conversion Rate, Retention Rate, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Average Precision (MAP), and F1 Score. While significant progress has been made, future work should focus on scaling RL models, addressing cold-start problems, and exploring hybrid approaches that combine RL with traditional recommendation systems.
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