公开(公告)号：US12175504B2

公开(公告)日：2024-12-24

申请号：US18085034

申请日：2022-12-20

Applicant: Visa International Service Association

Inventor： Yan Zheng ,  Yuwei Wang ,  Wei Zhang ,  Michael Yeh ,  Liang Wang

IPC: G06Q30/00 ,  G06Q30/0201 ,  G06Q30/0282

Abstract: Embodiments for training a recommendation system to provide merchant recommendations comprise receiving, by a processor, raw merchant embeddings and raw user embeddings generated from payment transaction records, wherein the raw merchant embeddings include a plurality of embedded features. A generative adversarial network (GAN) is trained to generate modified merchant embeddings from the raw merchant embeddings, where the modified embeddings remove a location feature. Subsequent to training and responsive to receiving a request for merchant recommendations in the target location for the target user, the GAN and a trained preference model are used to generate a list of merchant rankings based on a new set of modified merchant embeddings, past preferences of a target user, and the target location to recommend merchants in the target location.

公开(公告)号：US11593847B2

公开(公告)日：2023-02-28

申请号：US16688847

申请日：2019-11-19

Applicant: Visa International Service Association

Inventor： Yan Zheng ,  Yuwei Wang ,  Wei Zhang ,  Michael Yeh ,  Liang Wang

IPC: G06Q30/00 ,  G06Q30/0282 ,  G06Q30/0201

Abstract: A computer-implemented method for providing merchant recommendations comprises receiving, by a processor, raw merchant embeddings and raw user embeddings generated from payment transaction records, wherein the raw merchant embeddings include a plurality of embedded features. A generative adversarial network (GAN) performs a disentanglement process on the raw merchant embeddings to remove an effect of an identified feature by generating modified merchant embeddings that are free of the identified feature and are aligned with other ones of the plurality of features. A list of merchant rankings is automatically generates based on the modified merchant embeddings, past preferences of a target user using the raw merchant embeddings, and a current location in which the merchant recommendations should be made. A list of merchant rankings is then provided to the target user.