generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on embeddings provided as representations of data from a product profile of a respective product and an entity profile of the entity, the representations including a structural vector, a textual vector, and a categorical vector, each embedding generated based on processing data from the product profile of the respective product and the entity profile of the entity,

generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and

inputting the total latent vector and the latent context vector to the recommender network, the recommender network including multiple output layers and trained by few-shot learning using a multi-task loss function, wherein each output layer corresponds to a respective task in a set of tasks and includes parameters that are optimized for the respective task during few-shot learning, wherein the multi-task loss function includes a set of loss functions, each loss function also corresponding to a respective task in the set of tasks, wherein during a local update the parameters of the recommender network are updated, and during a global update, parameters of the recommender network and the embedding module are updated, and wherein few-shot learning includes concurrent training of parameters of the recommender network and the embedding module; and