Neeraj Gandhi
Papers
1
Total Citations
1
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1
About
Neeraj Gandhi is a researcher whose work sits at the intersection of machine learning and intelligent systems, with a primary focus on reinforcement learning (RL) and its practical deployment. His most notable contribution explores how RL, grounded in the Markov Decision Process (MDP) and Q-learning, can be harnessed to build more adaptive and effective recommendation systems. By translating the core principles of agent-environment interaction into the domain of user preference modeling, Gandhi has helped bridge a critical gap between theoretical RL frameworks and real-world applications. His 2023 paper, "Reinforcement Learning and its application in making Recommendation System," has already garnered attention as a foundational reference for those seeking to move beyond static, rule-based recommenders toward dynamic, reward-maximizing models. This work positions him as an emerging voice in the ongoing effort to make AI systems more responsive and personalized. For students and researchers entering the field, Gandhi’s research offers a clear, applied pathway into one of machine learning’s most promising paradigms.
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Top Papers
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