Abstract: An electronic device is provided. The electronic device includes: a memory storing a neural network model; and a processor configured to: obtain a plurality of individual graphs based on an access history for a plurality of contents over a plurality of sessions; generate an integrated graph, in which the plurality of individual graphs are integrated, based on a connection relationship between nodes included in the plurality of individual graphs and a number of times each connection between the nodes is repeated; obtain a plurality of augmented graphs by augmenting each of the plurality of individual graphs based on the integrated graph; and train the neural network model to provide recommended content based on the plurality of augmented graphs.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store at least one command and a processor connected to the memory and configured to control the electronic apparatus. The processor is configured to, by executing the at least one command, obtain graph information for a plurality of users in which viewing content is arranged in a time sequence based on content viewing history information for a plurality of users, obtain attribute information for each content by inputting the graph information for the plurality of users to a Graph Neural Network (GNN) model, group the plurality of users into a plurality of viewing groups based on the graph information for the plurality of users and the attribute information for each content, and provide a recommended content based on content information corresponding to the plurality of viewing groups.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a storage and a processor to generate first training data by performing transformation for first original data based on at least one first transform function input according to a user input, store first metadata including the at least one first transform function in the storage, generate second training data by performing transformation for second original data based on at least one first transform function included in the stored first metadata, generate third training data by performing transformation for the second training data based on at least one second transform function input according to a user input, and store second metadata including the at least one first transform function and the at least one second transform function in the storage.