User segmentation using contrastive learning augmented by user-user scoring

A computer-implemented method comprising:

• generating a plurality of user vectors for a plurality of users from user data tracked by a listing platform, the user data comprising general behavioral data and category-specific behavioral data, the category-specific behavioral data corresponding to items on the listing platform within a specified item category;
• generating a user graph in which each node corresponds to a user from the plurality of users and nodes are connected with edges having edge weights corresponding to user-user scores determined by applying different weightings to different types of user interactions with items on the listing platform;
• employing the plurality of user vectors and the user graph as training data to train a neural network over a plurality of iterations to provide a trained neural network, each iteration comprising:
• selecting one or more positive samples and/or one or more negative samples based on node connections in the user graph;
• causing the neural network to generate a set of user embeddings from user vectors corresponding to each user from the one or more positive samples and/or one the one or more negative samples;
• determining a loss based on the set of user embeddings, and
• updating the neural network based on the loss;
• using the trained neural network to generate a plurality of user embeddings by providing at least a portion of the plurality of user vectors as input to the trained neural network;
• and
• generating a plurality of user segments by clustering user embeddings from the plurality of user embeddings.

A computer system comprising:

• a processor;
• and
• a computer storage medium storing computer-useable instructions that, when used by the processor, causes the computer system to perform operations comprising:
• generating training data comprising a plurality of user vectors and a plurality of user-user scores, the plurality of user vectors generated for a plurality of users based on user data tracked by a listing platform, the plurality of user-user scores generated between pairs of users from the plurality of users based on user interactions with items on the listing platform;
• training a neural network using the training data to provide a trained neural network;
• causing the neural network to generate a plurality of user embeddings from at least a portion of the user vectors;
• and
• generating a plurality of user segments by clustering user embeddings from the plurality of user embeddings.