Self-supervised learning through data augmentation for recommendation systems

A device comprising:

• a processor;
• and
• a memory coupled to the processor, with instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising:
• receiving training data that includes original training examples corresponding to a set of virtual experiences, where individual training examples comprise user features and item features;
• generating augmented training examples by modifying one or more of a user feature or an item feature from corresponding original training examples;
• extracting respective representation embeddings from the original training examples and the augmented training examples;
• and
• determining, by a machine-learning model, a loss function, wherein the machine-learning model includes a user tower and an item tower, wherein the user tower includes a first deep neural network (DNN) that generates user embeddings based on the user features and the item tower includes a second DNN that generates item embeddings based on the item features and wherein the loss function is determined such that:
• a first loss for the user tower between representation embeddings of individual original training examples and representation embeddings of corresponding augmented training examples is minimized;
• and
• a second loss for the item tower between the representation embeddings of individual original training examples and representation embeddings of non-corresponding augmented training examples is maximized.

A non-transitory computer-readable medium with instructions stored thereon that, when executed by one or more computers, cause the one or more computers to perform operations, the operations comprising:

• receiving training data that includes original training examples corresponding to a set of virtual experiences, where individual training examples comprise user features and item features;
• generating augmented training examples by modifying one or more of a user feature or an item feature from corresponding original training examples;
• extracting respective representation embeddings from the original training examples and the augmented training examples;
• and
• determining, by a machine-learning model, a loss function, wherein the machine-learning model includes a user tower and an item tower, wherein the user tower includes a first deep neural network (DNN) that generates user embeddings based on the user features and the item tower includes a second DNN that generates item embeddings based on the item features and wherein the loss function is determined such that:
• a first loss for the user tower between representation embeddings of individual original training examples and representation embeddings of corresponding augmented training examples is minimized;
• and
• a second loss for the item tower between the representation embeddings of individual original training examples and representation embeddings of non-corresponding augmented training examples is maximized.