View source for 20230153870. UNSUPERVISED EMBEDDINGS DISENTANGLEMENT USING A GAN FOR MERCHANT RECOMMENDATIONS simplified abstract (Visa International Service Association)

=UNSUPERVISED EMBEDDINGS DISENTANGLEMENT USING A GAN FOR MERCHANT RECOMMENDATIONS=


==Organization Name==

[[:Category:Visa International Service Association|Visa International Service Association]]


[[Category:Visa International Service Association]]

==Inventor(s)==

[[:Category:Yan Zheng of Sunnyvale CA (US)|Yan Zheng of Sunnyvale CA (US)]][[Category:Yan Zheng of Sunnyvale CA (US)]]

[[:Category:Yuwei Wang of San Francisco CA (US)|Yuwei Wang of San Francisco CA (US)]][[Category:Yuwei Wang of San Francisco CA (US)]]

[[:Category:Wei Zhang of Fremont CA (US)|Wei Zhang of Fremont CA (US)]][[Category:Wei Zhang of Fremont CA (US)]]

[[:Category:Michael Yeh of Palo Alto CA (US)|Michael Yeh of Palo Alto CA (US)]][[Category:Michael Yeh of Palo Alto CA (US)]]

[[:Category:Liang Wang of San Jose CA (US)|Liang Wang of San Jose CA (US)]][[Category:Liang Wang of San Jose CA (US)]]

==UNSUPERVISED EMBEDDINGS DISENTANGLEMENT USING A GAN FOR MERCHANT RECOMMENDATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230153870 titled 'UNSUPERVISED EMBEDDINGS DISENTANGLEMENT USING A GAN FOR MERCHANT RECOMMENDATIONS

==Simplified Explanation==

The patent application describes a method for training a recommendation system to provide merchant recommendations based on payment transaction records. 

* The system receives raw merchant embeddings and raw user embeddings generated from payment transaction records.
* A generative adversarial network (GAN) is trained to generate modified merchant embeddings by removing a location feature.
* The trained GAN and a preference model are used to generate a list of merchant rankings based on modified merchant embeddings, past user preferences, and the target location.
* The system can recommend merchants in the target location based on the generated rankings.

==Potential Applications==
* E-commerce platforms can use this technology to provide personalized merchant recommendations to users based on their preferences and location.
* Mobile payment apps can utilize this technology to suggest nearby merchants to users based on their transaction history and current location.

==Problems Solved==
* This technology solves the problem of providing accurate and relevant merchant recommendations to users based on their preferences and location.
* It addresses the challenge of removing location bias from merchant embeddings to ensure fair and unbiased recommendations.

==Benefits==
* Users can discover new merchants that align with their preferences and are conveniently located.
* Merchants can benefit from increased visibility and customer engagement through personalized recommendations.
* The system provides fair and unbiased recommendations by removing location bias from the merchant embeddings.


==Original Abstract Submitted==

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.



[[Category:G06Q30/0282]]
[[Category:G06Q30/0201]]