Natural Language Processing

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The recommendation systems (RS) are becoming an integral part of our daily lives. This means that we can obtain what we desire either through internet-accessible applications or on social media channels. Traditional views of the recommendation problem refer to it as a simple classification or prediction problem; however, recently new evidence indicates that it is essentially a sequential problem[1]. It can therefore be formulated as a Markov decision process (MDP) and reinforcement learning (RL) methods can be employed to resolve it [1]. RL algorithms play a crucial role as these algorithms are very advantageous to cope with the dynamic environment and large space [4]. Deep Reinforcement Learning (DRL), have enabled RL to be applied to the recommendation problem with massive states and action spaces. RL-based and DRL-based methods in a classified manner based on the specific RL algorithm, like Q-learning, SARSA, and REINFORCE, that is used to optimize the recommendation policy[2].