Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for selecting ads for a video. The program and method provide for receiving a request for an ad to insert into a video playing on a client device, the request including a first content identifier that identifies a first type of content included in the video; determining a set of content identifiers associated with the first content identifier, the set of content identifiers identifying second types of content to filter with respect to providing the ad in response to the request; selecting an ad from among plural ads, by filtering ads tagged with a second content identifier included in the set of content identifiers; and providing the selected ad as a response to the request.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for selecting ads for a video. The program and method provide for receiving a request for an ad to insert into a video playing on a client device, the request including a first content identifier that identifies a first type of content included in the video; determining a set of content identifiers associated with the first content identifier, the set of content identifiers identifying second types of content to filter with respect to providing the ad in response to the request; selecting an ad from among plural ads, by filtering ads tagged with a second content identifier included in the set of content identifiers; and providing the selected ad as a response to the request.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for selecting ads for a video. The program and method provide for receiving a request for an ad to insert into a video playing on a client device, the request including a first content identifier that identifies a first type of content included in the video; determining a set of content identifiers associated with the first content identifier, the set of content identifiers identifying second types of content to filter with respect to providing the ad in response to the request; selecting an ad from among plural ads, by filtering ads tagged with a second content identifier included in the set of content identifiers; and providing the selected ad as a response to the request.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.