Abstract: A total interaction method and device to compute an interaction relationship between multiple features in a recommendation system is provided. The total interaction method includes: adding a plurality of categorical feature vectors to a first matrix, wherein each of the categorical feature vectors includes a plurality of latent features; performing one of categorical feature interaction computation and latent feature interaction computation on the first matrix to generate a second matrix; transposing the second matrix to generate a transposed matrix; and performing the other one of the categorical feature interaction computation and the latent feature interaction computation on the transposed matrix to generate a total interaction result.

Abstract: An embedding table generation method and an embedding table condensation method are provided. The embedding table generation method includes: building an initial architecture of an embedding table corresponding to categorical data according to an initial feature dimension; performing model training on the embedding table with the initial architecture to generate initial content of the embedding table; computing a condensed feature dimension based on the initial content of the embedding table; building a new architecture of the embedding table according to the condensed feature dimension; and performing the model training on the embedding table with the new architecture to generate condensed content of the embedding table.

Abstract: A generation method and an index condensation method of an embedding table are disclosed. The generation method includes: establishing an initial structure of the embedding table corresponding to categorical data according to an initial index dimension; performing model training on the embedding table having the initial structure to generate an initial content; defining each initial index as one of an important index and a non-important index based on the initial content; keeping initial indices defined as the important index in a condensed index dimension; dividing, based on a preset compression rate, initial indices defined as the non-important index into at least one initial index group each mapped to a condensed index in the condensed index dimension; establishing a new structure of the embedding table according to the condensed index dimension; and performing the model training on the embedding table having the new structure to generate a condensed content.