公开(公告)号：US20250047944A1

公开(公告)日：2025-02-06

申请号：US18917798

申请日：2024-10-16

Applicant: Adeia Guides Inc.

Inventor： Kyle Miller

IPC: H04N21/466 ,  G06N3/088

Abstract: Systems and methods are described for selecting content item identifiers for display. The system may identify a set of content items that are likely to be requested in the future based on a history of content item requests. The system then selects a first plurality of content categories using a category selection neural net and selects a first set of recommended content items for the first plurality of content categories. The system increases a reward score for the first plurality of content categories based on receiving a request for a content item that is included in the first set of recommended content items. The system also decreases the reward score for the first plurality of content categories based on determining that the requested content item is included in the set of content items that are likely to be requested in the future. The neural net is trained based on the reward score of the first plurality of content categories to reinforce reward score maximization. The trained neural net is the used to select content items for display.

公开(公告)号：US12149789B2

公开(公告)日：2024-11-19

申请号：US18204497

申请日：2023-06-01

Applicant: Adeia Guides Inc.

Inventor： Kyle Miller

IPC: H04N21/466 ,  G06N3/088

Abstract: Systems and methods are described for selecting content item identifiers for display. The system may identify a set of content items that are likely to be requested in the future based on a history of content item requests. The system then selects a first plurality of content categories using a category selection neural net and selects a first set of recommended content items for the first plurality of content categories. The system increases a reward score for the first plurality of content categories based on receiving a request for a content item that is included in the first set of recommended content items. The system also decreases the reward score for the first plurality of content categories based on determining that the requested content item is included in the set of content items that are likely to be requested in the future. The neural net is trained based on the reward score of the first plurality of content categories to reinforce reward score maximization. The trained neural net is the used to select content items for display.

公开(公告)号：US12267543B2

公开(公告)日：2025-04-01

申请号：US18225015

申请日：2023-07-21

Applicant: Adeia Guides Inc.

Inventor： Kyle Miller

IPC: H04N21/262 ,  H04N21/442 ,  H04N21/482

Abstract: Systems and associated methods are described for providing content recommendations. The system selects a first plurality of subsets of content categories, each subset of content categories comprising a first number of content categories. The subsets are assigned reward scores based on content popularity and duplication. The subset are then iteratively modified to increase the rewards scores. If the reward scores are still low, the process is repeated by selecting a second plurality of subsets of content categories, each subset of content categories comprising a second number of content categories, different from first number.