Snap Inc.

Yanen Li

Ning Xu

Shibi He

Leonardo Ribas Machado Das Neves

Lawrence Jason Muhlstein

Fei Wu

A computer-implement method comprises: training a classifier with labeled data from a dataset; classifying, by the trained classifier, unlabeled data from the dataset; providing, by the classifier to a policy gradient, a reward signal for each data/query pair; transferring, by the classifier to a ranker, learning; training, by the policy gradient, the ranker; ranking data from the dataset based on a query; and retrieving data from the ranked data in response to the query.

Data retrieval using reinforced co-learning for semi-supervised ranking

A neural network system can select content based on user and item content embeddings in an approach that can be updated in real time on the user device without server support. Requests for content sent to the server can include an anonymous user embedding that includes data describing the user's inputs. The content that is nearest to the user embedding in a joint embedding space can be returned as suggested content.

Joint embedding content neural networks

Systems and methods are provided for generating a user click history table and a random bucket training table, generating training data for training a user-type-affinity machine learning model by combining the user click history table and the random bucket training table, and training the user-type-affinity machine learning model with the generated training data. The systems and methods further provide for generating a user click prediction table and generating user-type-affinity prediction values for each of the plurality of users by inputting the user click prediction table into the user-type-affinity machine learning model.

Growing community of inventors

Los Angeles, CA, United States of America

Yanen Li