Supreme Datta
Papers
1
Total Citations
1
H-Index
1
About
Supreme Datta is a researcher at the forefront of applying reinforcement learning (RL) to real-world systems, with a particular focus on recommendation engines. His work bridges the gap between theoretical Markov Decision Processes (MDPs) and practical Q-learning algorithms, demonstrating how intelligent agents can be trained to optimize user engagement by maximizing cumulative rewards in dynamic environments. By framing recommendation tasks as sequential decision-making problems, Datta has contributed to a paradigm shift in how personalized content is served, moving beyond static models to adaptive, reward-driven systems. His foundational paper, "Reinforcement Learning and its application in making Recommendation System," has garnered early citations, signaling its growing influence in the intersection of RL and information retrieval. Datta’s research is especially relevant for students and engineers seeking to build autonomous, learning-based recommendation platforms that continuously improve from user feedback. His work stands as a key reference for those exploring how agent-based learning can transform user-centric AI applications.
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Top Papers
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