Hulu, LLC

Lutfi Ilke Kaya

Chi Zhang

Christopher Russell Kehler

Bangsheng Tang

Tong Yang

Kang Kang

Jeff Beal

Zhebang Hua

Heng Su

Wenkui Ding

Jinyu Yao

In some embodiments, a method generates combinations of ad campaign content and media program campaign content and ranks the combinations of ad campaign content and media program content. The ranking is based on ad campaign content being shown with the media program campaign content. The method selects one or more of the combinations of ad campaign content and media program campaign content based on the ranking. Then, display of the one or more of the combinations of ad campaign content and media program campaign content is caused where the one or more of the combinations of ad campaign content and media program campaign content include ad campaign content shown with media program campaign content in a same area of a display.

Personalized content ranking using content received from different sources in a video delivery system

In one embodiment, a method generates actions for entities found on a video delivery system based on information for user behavior of a user on the video delivery system and generates probabilities for the actions for the entities based on the actions for the entities and the user behavior. A probability for an action indicates the probability the user would select that action for an entity when compared against other actions in the set of actions for the set of entities. The method then selects an action feed based on the probabilities for the set of actions. The action feed includes at least a portion of the actions for the entities. The action feed is outputted to the client for display on an interface where an action on an entity in the action feed is performed by the video delivery system when selected by the user on the interface.

Generation and selection of actions for entities in a video delivery system

A video delivery service may display different content on an interface, such as ad campaign content, media program campaign content, and recommendation content. For example, the video delivery service may communicate with an ad engine to determine ad campaign content that is available and ad campaign scores to rate the ad campaign content. The video delivery service may also communicate with a media program campaign engine to determine any media program campaign content for the user and media program campaign scores to rate the media program campaign content. Then, the video delivery service may communicate with a recommendation engine to rate the relevance of the ad campaign content or media program campaign content to the user. After receiving the ad campaign content, media program campaign content, and recommendation content, the video delivery service determines which of the ad campaign content, media program campaign content, and recommendation content to display in the interface.

In one embodiment, a method generates a plurality of sub-relevance tables including a first set of relevance values between media programs. Each table models relevance values for a single feature in a plurality of features. Labeling results are received that include a second set of relevance values between the media programs. The method combines the sub-relevance tables into a single relevance table that includes a third set of relevance values between the media programs for the plurality of features. The combining generates weights for each of the sub-relevance tables based on the second set of relevance values for the labeling results and the first set of relevance values of the sub-relevance tables that are used to generate the third set of relevance values. A recommendation is provided to a user using the third set of relevance values from the single relevance table and a characteristic of the user.

Relevance table aggregation in a database system for providing video recommendations

In one embodiment, a method sends videos to a user that is using a video delivery service. The method receives user behavior that includes actions taken by the user on the video delivery service. The method inputs the user behavior into a first predictor to generate a set of actions for a set of entities. Also, the method inputs the set of actions for the set of entities, a real-time context, and the user behavior into a second predictor to generate probabilities for the set of actions for the set of entities. A probability for an action indicates the probability the user would select that action for an entity when compared against other actions in the set of actions for the set of entities. An action feed is selected based on the ranking and dynamically output to a client while the user is using the video delivery service.

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Santa Monica, CA, United States of America

Lutfi Ilke Kaya