Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for utilizing a parameterized notebook to adaptively generate visualizations regarding machine-learning models. In particular, the disclosed systems can generate a parameterized notebook based on a user-defined visualization recipe and provide parameter values that correspond to the machine-learning model to the parameterized notebook. Upon execution of the user-defined visualization recipe via the parameterized notebook, the disclosed systems can extract visualization data corresponding to the machine-learning model from the parameterized notebook. In addition, the disclosed systems can generate visualizations based on the visualization data and provide the generated visualizations for display in a graphical user interface.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for utilizing a parameterized notebook to adaptively generate visualizations regarding machine-learning models. In particular, the disclosed systems can generate a parameterized notebook based on a user-defined visualization recipe and provide parameter values that correspond to the machine-learning model to the parameterized notebook. Upon execution of the user-defined visualization recipe via the parameterized notebook, the disclosed systems can extract visualization data corresponding to the machine-learning model from the parameterized notebook. In addition, the disclosed systems can generate visualizations based on the visualization data and provide the generated visualizations for display in a graphical user interface.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an LSTM autoencoder model that captures a history of changes to user trait data. For example, the user embeddings system identifies user trait changes from the user trait data over time as well as generates user trait sequences. Further, the user embeddings system can utilize the user trait sequences to train an LSTM neural network in a semi-supervised manner and generate uniform user embeddings for users.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an LSTM autoencoder model that captures a history of changes to user trait data. For example, the user embeddings system identifies user trait changes from the user trait data over time as well as generates user trait sequences. Further, the user embeddings system can utilize the user trait sequences to train an LSTM neural network in a semi-supervised manner and generate uniform user embeddings for users.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users.