Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, upon request for a trait-intersection count of users (or other digital entities) corresponding to traits for a target time period, use a machine-learning model to analyze a semantic-trait embedding of the traits and to generate an estimated trait-intersection count of such entities sharing the traits for the target time period. By applying a machine-learning model trained to estimate trait-intersection counts, the disclosed methods, non-transitory computer readable media, and systems can analyze both a semantic-trait embedding of traits and an initial trait-intersection count of trait-sharing entities for an initial time period to estimate the trait-intersection count for the target time period.

Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently predicting a persona class of a client device and/or target user utilizing an overlap-agnostic machine learning model and distributing persona-based digital content to the client device. In particular, in one or more embodiments, the persona classification system can learn overlap-agnostic machine learning model parameters to apply to user traits in real-time or in offline batches. For example, the persona classification system can train and utilize an overlap-agnostic machine learning model that includes an overlap-agnostic embedding model, a trained user-embedding generation model, and a trained persona prediction model. By applying the learned overlap-agnostic machine learning model parameters to the target user traits, the persona classification system can predict a persona class for sending digital content based on the predicted persona class.

Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, upon request for a trait-intersection count of users (or other digital entities) corresponding to traits for a target time period, use a machine-learning model to analyze a semantic-trait embedding of the traits and to generate an estimated trait-intersection count of such entities sharing the traits for the target time period. By applying a machine-learning model trained to estimate trait-intersection counts, the disclosed methods, non-transitory computer readable media, and systems can analyze both a semantic-trait embedding of traits and an initial trait-intersection count of trait-sharing entities for an initial time period to estimate the trait-intersection count for the target time period.