公开(公告)号：US20240112041A1

公开(公告)日：2024-04-04

申请号：US17937497

申请日：2022-10-03

Applicant: ROKU, INC

Inventor： Abhishek BAMBHA ,  Rohit MAHTO ,  Nam VO ,  Zidong WANG ,  Fei XIAO

IPC: G06N5/02 ,  G06N5/04

CPC classification number: G06N5/02 ,  G06N5/048

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for stochastic candidate selection for content recommendation. An example embodiment operates by a computer-implemented method for stochastic candidate selection for content recommendation. The method includes receiving, by at least one computer processor, a first plurality of content candidates and selecting a second plurality of content candidates from the first plurality of content candidates. The method further include ranking the second plurality of content candidates based on one or more parameters and selecting a third plurality of content candidates from the ranked second plurality of content candidates. The method can further include displaying the third plurality of content candidates using a display device.

公开(公告)号：US20250133251A1

公开(公告)日：2025-04-24

申请号：US18988182

申请日：2024-12-19

Applicant: ROKU, INC.

Inventor： Fei XIAO ,  Zidong WANG ,  Jose SANCHEZ ,  Abhishek BAMBHA ,  Ronica JETHWA

IPC: H04N21/25 ,  H04N21/258

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

公开(公告)号：US20240127106A1

公开(公告)日：2024-04-18

申请号：US17965284

申请日：2022-10-13

Applicant: Roku, Inc.

Inventor： Abhishek BAMBHA ,  Weicong DING ,  Zidong WANG ,  Fei XIAO

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for online automatic hyperparameter tuning of a machine learning model that provides a user experience to media devices such that the machine learning model maximizes (or minimizes) an objective function. An example embodiment operates by generating an initial set of hyperparameter configurations for a machine learning model based on sampling data received from media devices over a network. The embodiment then determines, using an hyperparameter tuning method, a hyperparameter configuration based on the initial set of hyperparameter configurations that causes a training of the machine learning model using a learning algorithm to maximize an objective function. The embodiment then trains the machine learning model according to the determined hyperparameter configuration using the learning algorithm. The embodiment then provides, using the trained machine learning model, a user experience to the media devices.