Hulu, LLC

Microsoft Technology Licensing, LLC

Christopher Russell Kehler

Chi Zhang

Lutfi Ilke Kaya

Dmitriy Meyerzon

Ashok Kuppusamy

Victor Poznanski

Charles Reeves Little, Ii

Oleg Melnychuk

Joshua Stickler

Joshua Cook

Lacey Anne Donohue

Charles Francis Moran

Tong Yang

Bangsheng Tang

Kang Kang

Zhebang Hua

Jeff Beal

Xiaohui Xie

Yunsheng Jiang

Jiarui Yang

Brian T Morrison

Kaiwen Deng

In some embodiments, a method receives a list of ranked entities for playback. The list of ranked entities being ranked in a first order based on information for a user account. At least a portion of the list of ranked entities is used to determine corresponding channels that are playing the at least the portion of the list of ranked entities. The method rearranges the channels of an electronic programming guide from a sequence to a second order based on the first order for the at least the portion of the list of ranked entities. The channels are displayed in the electronic programming guide based on the second order.

Displaying an electronic programming guide for live television

Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Aggregating enterprise graph content around user-generated topics

In some embodiments, a method uses a first list of ranked entities to determine a first ordering of entities in slots of an electronic programming guide. Information is outputted to display at least a portion of the first ordering of entities in slots of the electronic programming guide. The method determines when a refresh of the electronic programming guide should be performed. When it is determined that the refresh should be performed, if a second list of ranked entities is available: the method uses the second list of ranked entities to determine a second ordering of entities in slots of an electronic programming guide and outputs information to display at least a portion of the second ordering of entities in slots of the electronic programming guide to update the electronic programming guide for the refresh.

In some embodiments, a method selects a sequence of programs watched by a user account. The method calculates a first set of weights based on comparing content of the sequence of programs to content of a target program and calculates a second set of weights based on an order of the sequence of the programs and the first of weights. The first set of weights and the second set of weights are applied to the sequence of programs to generate a prediction of a similarity of the sequence of programs to the target program. Then, the method outputs the prediction of the similarity for use in determining a recommendation for the user account.

Watch sequence modeling for recommendation ranking

In some embodiments, a method receives a list of ranked entities for video playback for a first interface in a first layout. The list of ranked entities being ranked based on personalized information for a user account. The list of ranked entities is used to determine an ordering of entities in slots of an electronic programming guide. The slots of the electronic programming guide display the entities in a second layout based on a time dimension. The method outputs information to display at least a portion of the entities in the electronic programming guide based on the ordering of entities.

Personalized ranking of entities in an electronic programming guide for live television

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 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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Seattle, WA, United States of America

Christopher Russell Kehler