Reinforcement Learning and its application in making Recommendation System

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents are made to take actions in an environment in order to maximize the total reward. RL works on Markov Decision Process which leads to Q-learning. MDP provides a mechanism to maximize the reward in a given environment. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. DRL has applications in many fields like medicine, robotics, games, etc. Combining DL and RL leads to the formation of Deep Q- Networks. Another application of RL and the focus of this seminar is personalized recommendation systems. Recommendation systems are trained on user-item interaction to predict the next item that a user can be interested in. In personalized recommendation systems, it is important to consider a lot of factors like user-item interactions, clicks, purchases, loss, etc. DRL does a very good job of reducing the loss in recommendation systems and recommending relevant items. I will explore a technique called self-supervised learning in which a model is made to predict data for itself for its use in recommendation systems. I also summarize 2 frameworks namely Self- Supervised Q-learning (SQN) and Self-Supervised Actor-Critic (SAC) based on self-supervised learning and try to understand how they work differently as compared to non-RL recommendation systems.Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents are made to take actions in an environment in order to maximize the total reward. RL works on Markov Decision Process which leads to Q-learning. MDP provides a mechanism to maximize the reward in a given environment. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. DRL has applications in many fields like medicine, robotics, games, etc. Combining DL and RL leads to the formation of Deep Q- Networks. Another application of RL and the focus of this seminar is personalized recommendation systems. Recommendation systems are trained on user-item interaction to predict the next item that a user can be interested in. In personalized recommendation systems, it is important to consider a lot of factors like user-item interactions, clicks, purchases, loss, etc. DRL does a very good job of reducing the loss in recommendation systems and recommending relevant items. I will explore a technique called self-supervised learning in which a model is made to predict data for itself for its use in recommendation systems. I also summarize 2 frameworks namely Self- Supervised Q-learning (SQN) and Self-Supervised Actor-Critic (SAC) based on self-supervised learning and try to understand how they work differently as compared to non-RL recommendation systems