Machine learning engine for identification of related vertical groupings

Patent metadata

Field	Value
Publication number	US-2020005192-A1
Application number	US-201816024225-A
Country	US
Kind code	A1
Filing date	Jun 29, 2018
Priority date	Jun 29, 2018
Publication date	Jan 2, 2020
Grant date	—

Abstract

A machine learning engine for identification of related vertical groupings may be trained using artificial intelligence and machine techniques and used according to techniques discussed herein. A consumer account may be used to process transactions electronically with merchants. The consumer account may therefore be linked to a transaction history, which may be processed to identify the consumer's vertical transaction list for verticals of previous transactions. This may be aggregated for a merchant used by the consumer, and may be weighted before sending back to the consumer. Multiple iterations of aggregating and weighing the merchant and consumer lists may be applied to determine highest ranked verticals for consumers and merchants based on multiple degrees of separation between certain merchants and consumers. Using the weighted lists, verticals may be identified for consumers that the consumer may not have previously transacted within, which may be used to provide a recommendation.

First claim

What is claimed is:

1 . A system comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving, from a first device, a first vertical list of a first set of transaction verticals for first transactions processed with a merchant by a first account associated with the first device, wherein the first vertical list comprises the first set of transaction verticals and a first plurality of numbers of the first transactions processed by the first account in each of the first set of transaction verticals;
receiving, from a second device, a second vertical list of a second set of transaction verticals for second transactions processed with the merchant by a second account associated with the second device, wherein the second vertical list comprises the second set of transaction verticals and a second plurality of numbers of the second transactions processed by the second account in each of the second set of transaction verticals;
determining, using a machine learning engine, a first merchant vertical list for the merchant based on the first vertical list and the second vertical list, wherein the first merchant vertical list comprises merchant transaction verticals for the first set of transaction verticals and the second set of transaction verticals, and wherein the first merchant vertical list further comprises a third plurality of numbers of the first transactions and the second transactions in each of the merchant transaction verticals; and
transmitting the first merchant vertical list to the first account and the second account.

2 . The system of claim 1 , wherein the operations further comprise:
determining, using the machine learning engine, an association between a first vertical and a second vertical in the first merchant vertical list; and generating, by the machine learning engine, a recommendation rule based on the association.

3 . The system of claim 2 , wherein operations further comprise:
determining that the first vertical list comprises the first vertical; generating a recommendation associated with the second vertical based on the recommendation rule; and providing the recommendation to the first account.

4 . The system of claim 3 , wherein the association comprises a highest number of shared transactions between the first vertical and the second vertical or a churn rate in the first vertical.

5 . The system of claim 3 , wherein the operations further comprise:
receiving a response to the recommendation from the first device; and updating the machine learning engine based on the response.

6 . The system of claim 3 , wherein the second vertical list for the second account comprises the second vertical, and wherein the recommendation is further based on a peer similarity between the first account and the second account.

7 . The system of claim 3 , wherein the recommendation is further provided based on a peer similarity between the first account, the second account, and a third account, wherein a third vertical list for the third account comprises the second vertical, wherein the first vertical list and the second vertical list share the first vertical, and wherein the second vertical list and the third vertical list share a third vertical.

8 . The system of claim 2 , wherein the recommendation rule groups the first vertical with the second vertical and identifies the second vertical to the machine learning engine based on a threshold number of transactions by one of the first account, the second account, or a third account in the first vertical.

9 . The system of claim 8 , wherein the operations further comprise
receiving, from the first account, a first aggregated vertical list for the first account, wherein the first aggregated vertical list comprises the merchant vertical list aggregated with a second merchant vertical list received by the first account from a second merchant; receiving, from the second account, a second aggregated vertical list for the second account, wherein the second aggregated vertical list comprises the merchant vertical list aggregated with a third merchant vertical list received by the second account from a third merchant; and updating the first merchant vertical list based on the first aggregated vertical list and the second aggregated vertical list.

10 . The system of claim 9 , wherein the operations further comprise:
performing a plurality of iterations of receiving aggregates of a plurality of vertical lists from the first device and the second device until a maximum number of iterations.

11 . The system of claim 9 , wherein the determining the first merchant vertical list is based on a weight applied to the merchant transaction verticals, and wherein the updating the first merchant vertical list is based on the weight.

12 . A method comprising:
accessing transaction data, wherein the transaction data comprises data associated with transactions between a plurality of users and a first entity for items provided by the first entity in a set of verticals; determining a vertical list based on the transaction data, wherein the vertical list comprises a first number of the transactions in each of the set of verticals by the plurality of users; weighting the vertical list based a weight applied to the first number for the each of the set of verticals; transmitting the vertical list to a computing device associated with each of the users; receiving, from the computing device associated with each of the users, an aggregated vertical list, wherein the aggregated vertical list comprises the vertical list and an additional vertical comprising a second number of additional transactions with a second entity by the each of the users; and updating the vertical list based on the aggregated vertical list from each of the users.

13 . The method of claim 12 , wherein the weight is selected by the first entity or determined by a service provider for the first entity, and wherein the weight is applied to each of the first number of the transactions.

14 . The method of claim 12 , further comprising:
determining, by a machine learning engine, a plurality of recommendation rules based on the vertical list, wherein the plurality of recommendation rules identify groups of the set of verticals in the vertical list based on the transactions sharing common users.

15 . The method of claim 14 , further comprising:
determining one of the groups for a user based on a transaction history for the user in the one of the groups; determining, by the machine learning engine, a shared vertical within the one of the groups based on the vertical list and the plurality of recommendation rules; and providing a recommendation for an item associated with the shared vertical.

16 . The method of claim 15 , wherein the one of the groups is determined based on a most common vertical for the user in the transaction history, and wherein the shared vertical is a next highest rated vertical in the one of the groups.

17 . A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
compiling, by a service provider, a merchant vertical transaction list for a first merchant, wherein the merchant vertical transaction list comprises a first vertical having a first number of transactions processed by the first merchant and a second vertical having a second numbe

Assignees

• Paypal Inc

Inventors

• Kumar Dinesh
• Melanathurbhaskar Geethamanjusha

Classifications

• G06Q30/0631

  Recommending goods or services · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06N5/025 Primary

  Extracting rules from data · CPC title

• G06N99/005 Primary

  Physics · mapped topic