Abstract: Aspects of the invention include a computer-implemented method for generating product recommendations used a hypergraph. The computer-implemented method includes generating a product embedding vector for a product unpurchased by a consumer based on a hypergraph. Generating a consumer embedding vector for the consumer based on the hypergraph. An affinity between the consumer and a product unpurchased by the consumer is scored based on the product embedding vector and the consumer embedding vector. A digital description of the unpurchased product is retrieved based on the scoring. The digital description is presented to the consumer.

Abstract: Aspects of the invention include a computer-implemented method for generating product recommendations used a hypergraph. The computer-implemented method includes generating a product embedding vector for a product unpurchased by a consumer based on a hypergraph. Generating a consumer embedding vector for the consumer based on the hypergraph. An affinity between the consumer and a product unpurchased by the consumer is scored based on the product embedding vector and the consumer embedding vector. A digital description of the unpurchased product is retrieved based on the scoring. The digital description is presented to the consumer.

Abstract: At least one original data matrix is received, wherein the at least one original data matrix includes information of at least one user. At least one submatrix is sampled from the at least one original data matrix, wherein the at least one submatrix includes at least part of information of the at least one user. At least one matrix approximation sub-model is generated for the at least one submatrix based on a trained matrix approximation model, wherein the matrix approximation sub-model captures some preferences of the at least one user.