AP-TRL: Augmenting Real-Time Personalization with Transformer Reinforcement Learning
Ujjwal Gupta, Yeshwanth Nagaraj · 2023
In the digital era, understanding user behavior in real-time and providing immediate personalization can significantly enhance the user experience and engagement. This paper introduces a novel approach that integrates the transformer architecture with reinforcement learning (RL) for real-time user behavior tracking, and recommendation. This paper demonstrates how this hybrid model can efficiently categorize user actions, predict future behaviors, and personalize content in real-time. Experimental results show that our model outperforms traditional methods, with a marked improvement in accuracy and response time.