公开(公告)号：US20250053853A1

公开(公告)日：2025-02-13

申请号：US18232468

申请日：2023-08-10

Applicant: ROKU, INC.

Inventor： FEI XIAO ,  ZIDONG WANG ,  LIAN LIU ,  NAM VO ,  WEICONG DING ,  ABHISHEK BAMBHA ,  AMIT VERMA ,  AASISH SIPANI ,  ROHIT MAHTO ,  HOSSEIN DABIRIAN ,  JOSE SANCHEZ

IPC: G06N20/00

Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

公开(公告)号：US20240276041A1

公开(公告)日：2024-08-15

申请号：US18107858

申请日：2023-02-09

Applicant: ROKU, INC.

Inventor： FEI XIAO ,  PULKIT AGGARWAL ,  ABHISHEK BAMBHA ,  ANIRBAN DAS ,  RONICA JETHWA ,  LIAN LIU ,  ROHIT MAHTO ,  JOSE SANCHEZ ,  AMIT VERMA ,  NAM VO ,  YING ZHAO

IPC: H04N21/25 ,  G06N20/00 ,  H04N21/258

CPC classification number: H04N21/251 ,  G06N20/00 ,  H04N21/25883

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.