公开(公告)号：US12067607B2

公开(公告)日：2024-08-20

申请号：US18167961

申请日：2023-02-13

Applicant: YAHOO ASSETS LLC

Inventor： Suleyman Cetintas ,  Xian Wu ,  Jian Yang

IPC: G06Q30/00 ,  G06F16/22 ,  G06F16/901 ,  G06F16/9535 ,  G06N20/00 ,  G06Q10/087 ,  G06Q30/0204 ,  G06Q30/0282 ,  G06Q30/0601 ,  G06Q50/00

CPC classification number: G06Q30/0631 ,  G06F16/2237 ,  G06F16/2264 ,  G06F16/9024 ,  G06F16/9535 ,  G06N20/00 ,  G06Q10/087 ,  G06Q30/0204 ,  G06Q30/0282 ,  G06Q30/0625 ,  G06Q50/01

Abstract: Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).

公开(公告)号：US11587143B2

公开(公告)日：2023-02-21

申请号：US17466879

申请日：2021-09-03

Applicant: YAHOO ASSETS LLC

Inventor： Suleyman Cetintas ,  Xian Wu ,  Jian Yang

IPC: G06Q30/00 ,  G06Q30/0601 ,  G06F16/9535 ,  G06F16/901 ,  G06F16/22 ,  G06N20/00 ,  G06Q10/087 ,  G06Q30/0204 ,  G06Q50/00 ,  G06Q30/0282

Abstract: Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).

公开(公告)号：US12223399B2

公开(公告)日：2025-02-11

申请号：US17083020

申请日：2020-10-28

Applicant: YAHOO ASSETS LLC

Inventor： Suleyman Cetintas ,  Xian Wu

IPC: G06N20/00 ,  G06F16/903

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Upon receiving input data associated with a time series, hidden representations associated with the time series in a feature space are obtained and used to generate a query vector in a query space. Such generated query vector is then used to query relevant historic information related to the time series. The query vector and the relevant historic information are aggregated to generate at least one queried vector, which is aggregated with the hidden representations to generate enriched hidden representations that enhance the expressiveness of the hidden representations.

公开(公告)号：US20230410131A1

公开(公告)日：2023-12-21

申请号：US18242294

申请日：2023-09-05

Applicant: Yahoo Assets LLC

Inventor： Suleyman Cetintas ,  Pengyang Wang

IPC: G06Q30/0202

CPC classification number: G06Q30/0202 ,  H04L67/535

Abstract: A method includes executing operations to generate a first enhancement function based on a parent-child link in a content hierarchy including a link between a parent node in a first level of the content hierarchy to a child node in a second level of the content hierarchy below the first level. A second enhancement function is generated based on a sibling link in the content hierarchy including a link between a sibling node in a third level of the content hierarchy and a sibling node in the third level of the content hierarchy sharing a common parent node with the first sibling node in a fourth level of the content hierarchy above the third level. A user content consumption metric is generated based on the first and second enhancement functions. A content list including a set of candidate content items ranked based on the user content consumption metric is generated.