Generating user embedding representations that capture a history of changes to user trait data

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:

• receive user traits for a plurality of users that correspond to a plurality of timestamps;
• generate a user trait sequence for each user of the plurality of users that comprises changes to user traits associated with the user with respect to the plurality of timestamps;
• and
• generate user embeddings for the plurality of users from the user trait sequences utilizing a long short-term memory (LSTM) neural network that encodes the changes to the user traits over time within each of the user trait sequences into the user embeddings, wherein the LSTM neural network is an encoder within an LSTM autoencoder model having the encoder and a decoder, and wherein the encoder and the decoder each utilize one or more LSTM cells.

A system for generating user embeddings that represent a journey of a user's traits over time comprising:

• at least one processor;
• a memory that comprises:
• user traits for a plurality of users that correspond to a plurality of timestamps;
• a user traits dictionary that comprises user traits mapped to identifiers;
• and
• a long short-term memory (LSTM) autoencoder model comprising an embedding layer, an LSTM encoder neural network having a first set of LSTM cells, an LSTM decoder neural network having a second set of LSTM cells, and a classification layer;
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• generate a user trait sequence for each user of the plurality of users by indicating additions and removals of user traits associated with the user over the plurality of timestamps;
• and
• utilize the LSTM encoder neural network of the LSTM autoencoder model to generate user embeddings for each user of the plurality of users by encoding user traits changes over time within each of the user trait sequences into the user embeddings.