Abstract: A method of providing a recommendation based on reinforcement learning includes: obtaining user data; generating a simulated user corresponding to an actual user based on the user data; determining an action based on a state of the simulated user, where the action corresponds to a recommendation element; updating the state of the simulated user; based on the updating, identifying the state of the simulated user and a reward output from the simulated user based on the recommendation element; generating a recommendation session by determining a plurality of the recommendation elements based on the state of the simulated user and the reward; and outputting the recommendation session.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.

Abstract: Provided are an artificial intelligence (AI) system simulating a function of a human brain, such as cognition and judgment, using a machine learning algorithm, such as deep learning, and an application thereof. Also, provided is a method, performed by an electronic apparatus, of refining an artificial intelligence (AI) model, the method including: detecting information about a context of an electronic apparatus used to refine a local model stored in the electronic apparatus being changed; determining a gradient for refining the local model based on the changed information about the context; refining the local model based on the determined gradient; transmitting the gradient to a server; receiving, from the server, information about a global model refined based on the gradient; and refining the local model based on the received information.