公开(公告)号：US20230316353A1

公开(公告)日：2023-10-05

申请号：US17712406

申请日：2022-04-04

Applicant: ADOBE INC.

Inventor： Michele Saad ,  Matthew Cecil Zimmerman

IPC: G06Q30/06 ,  G06F16/22 ,  G06F16/28 ,  H03M7/30

CPC classification number: G06Q30/0603 ,  G06F16/2272 ,  G06F16/285 ,  H03M7/30

Abstract: An effective stock keeping unit (SKU) management system encodes catalog data into an embedding per catalog item. An embedding space is created by encoding catalog item data into an embedding per catalog item. The embedding is created by generating an index, where a number of rows represents a number of catalog items and a number of columns represents a number of fields associated with each catalog item. The index is then denormalized using customer groups and transformed by compressing the number of columns, to create the embedding space. In some configuration, a machine learning model is trained using catalog data. In the embedding space, item similarity is encoded by clustering catalog SKUs into groups in the embedding space, by placing similarly related items close to each other in the embedding space. Catalog items are then searched for in the embedding, with the closest clusters searched for a particular catalog item.

公开(公告)号：US20230350963A1

公开(公告)日：2023-11-02

申请号：US17731999

申请日：2022-04-28

Applicant: ADOBE INC.

Inventor： Michele Saad ,  Matthew Cecil Zimmerman

IPC: G06F16/954 ,  G06N20/00 ,  G06F16/903 ,  G06Q30/06

CPC classification number: G06F16/954 ,  G06N20/00 ,  G06F16/90335 ,  G06Q30/0623

Abstract: A system leverages reinforcement learning techniques to determine distribution of items to listing platforms and search ranking rules for each listing platform. Using historical listing data regarding items listed at one or more listing platforms, a machine learning model generates item interaction data, and a reinforcement learning agent is initialized using the item interaction data. The reinforcement learning agent is trained to optimize a function for selecting item distributions and search ranking rules across listing platforms. At each epoch of a series of epochs, the function is used to select an action including a new distribution of items to listing platforms and new search ranking rules to use at each listing platform. After the action from an epoch is implemented, the reinforcement learning agent updates the function, for instance, based on an impact of the action.

公开(公告)号：US12169856B2

公开(公告)日：2024-12-17

申请号：US17712406

申请日：2022-04-04

Applicant: ADOBE INC.

Inventor： Michele Saad ,  Matthew Cecil Zimmerman

IPC: G06F7/00 ,  G06F16/22 ,  G06F16/245 ,  G06F16/28 ,  G06Q30/0601 ,  H03M7/30

Abstract: An effective stock keeping unit (SKU) management system encodes catalog data into an embedding per catalog item. An embedding space is created by encoding catalog item data into an embedding per catalog item. The embedding is created by generating an index, where a number of rows represents a number of catalog items and a number of columns represents a number of fields associated with each catalog item. The index is then denormalized using customer groups and transformed by compressing the number of columns, to create the embedding space. In some configuration, a machine learning model is trained using catalog data. In the embedding space, item similarity is encoded by clustering catalog SKUs into groups in the embedding space, by placing similarly related items close to each other in the embedding space. Catalog items are then searched for in the embedding, with the closest clusters searched for a particular catalog item.