Hulu, LLC

Brian T Morrison

Xiaohui Xie

Yunsheng Jiang

Bo Liu

Peng Wang

Christopher Russell Kehler

Brandon Lonac

Brian Burkhart

Jiarui Yang

Lena Bareket

Darshana Umakanth

Yaqi Wang

Kaiwen Deng

Huimin Chen

Carlos Sandoval

David Jung

Diman Shen

Yuchong Luo

Andre Xiong

In some embodiments, a method receives a request for an interface. A configuration for the interface includes a plurality of slots that are associated with a plurality of collection pools, wherein each collection pool is configurable with a ranking strategy. The method determines a collection pool for a slot in the interface where the collection pool is associated with a plurality of collections. A collection is received for the slot. The collection is determined by ranking the plurality of collections using a ranking strategy for the collection pool to generate a ranking of the plurality of collections. The method outputs the collection for the collection pool for display in the slot of the interface.

Interface generation using collection pools

In some embodiments, a method receives a history of videos that were viewed on a video delivery system as a first sentence in a sequential order and a target video as a second sentence as input to a prediction network. The prediction network analyzes representations for the history of videos and a representation of the target video. The prediction network generates a session representation based on bidirectionally analyzing a sequence of the first representations and the second representation. The method uses the session representation to determine whether to recommend the target video.

Content recommendation generation using content based on bi-directional prediction

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

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Burien, WA, United States of America

Brian T Morrison