公开(公告)号：US20240214619A1

公开(公告)日：2024-06-27

申请号：US18089343

申请日：2022-12-27

Applicant: ROKU, INC.

Inventor： FEI XIAO ,  RONICA JETHWA ,  JING YE ,  ABHISHEK BAMBHA ,  ZIDONG WANG ,  JOSE SANCHEZ ,  NAM VO ,  KHALDUN AIDARABSAH ,  PULKIT AGGARWAL ,  LIAN LIU ,  ANIRBAN DAS ,  ROHIT MAHTO

IPC: H04N21/25 ,  H04N21/24 ,  H04N21/472

CPC classification number: H04N21/252 ,  H04N21/2407 ,  H04N21/472

Abstract: A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

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.