Distance-based pair loss for collaborative filtering

A system for training a set of user embeddings and a set of item embeddings for user-item recommendation, comprising:

• a processor that executes instructions;
• a non-transitory computer-readable medium having instructions executable by the processor for:
• identifying a training set of user-item pairs for a respective user of a set of users with respect to a respective item of a set of items, the user-item pairs including positive user-item pairs labeled with a positive interaction type and negative user-item pairs labeled with a negative interaction type;
• determining, for each user-item pair in the training set of user-item pairs, a distance between a user embedding, of a plurality of user embeddings, associated with the respective user and an item embedding, of a plurality of item embeddings, associated with the respective item;
• determining a weight for each user-item pair in the training set of user-item pairs based on the distance of the user-item pair and the distance of at least one other user-item pair labeled with the same interaction type;
• and
• training the plurality of user embeddings and the plurality of item embeddings based on the training set of user-item pairs and determined weight of each user-item pair.