Method and device for providing a recommender system

A computer implemented method for providing a recommender system for recommending an item out of a variety of items to a user,

• the recommender system using an architecture with an encoder network (BA) and a decoder network
• the encoder network comprising a graph neural network, providing encodings of users and items;
• the users and items being represented by nodes, whereby a user node is connected with a subset of the item nodes;
• the encodings capturing relations between a center node and other nodes;
• and
• the decoder network extracting, for each item, from the encodings a score that an item is connected to the user;
• the recommender system being trained by performing the following training steps:
• a. providing, for each user node and each item node an initial encoding the initial encoding being used as trainable parameters;
• b. using, by the encoder network, adjacency information and the initial encodings as input to produce contextualized encodings of each item and each user node,
• the adjacency information gained from the connections between nodes, and
• the contextualized encodings reflecting the relations between a center node and other nodes,
• whereby at least a part of the weights of the encoder network is initialized in a random manner;
• c. obtaining, for all users, from the contextualized encodings for each item, by the decoder network a prediction whether an item node is connected to a specific user node, the prediction being expressed by a score assigned to the respective item node;
• d. calculating a loss function for that prediction, whereby the loss function defined such that, in the prediction, items for which an item node has a connection to the user node are assigned a higher score than items for which there is no connection;
• e. deriving from the loss function at least partly a gradient;
• f. determining an effect of the trainable parameters on the loss function by using the gradient;
• g. adapting the trainable parameters by use of the gradient such that the loss function is minimized;
• and
• h. repeating the steps a) through g) until a training exit criterion is met, whereby no adaptations to the weights of the encoder network are made due to the training.