Recommendation engine

Patent metadata

Field	Value
Publication number	US-2016117752-A1
Application number	US-201514610521-A
Country	US
Kind code	A1
Filing date	Jan 30, 2015
Priority date	Oct 27, 2014
Publication date	Apr 28, 2016
Grant date	—

Abstract

A method for providing recommendations for customers is described. The method comprises obtaining transaction data associated with one or more products being purchased by a customer. The method further comprises generating a customer household graph, wherein the customer household graph comprises a customer household node associated with the customer, a product superset node, and one or more product nodes associated with the product superset node. Further, at least one product metadata node associated with each of the one or more product nodes is determined. Further, at least one user node is associated with the customer household node based on the at least one product metadata node. Further, a user group node associated with the customer household node is determined based on the at least one user node. Further, one or more recommendations to be provided to the user are ascertained.

First claim

I/We claim:

1 . A recommendation engine comprising:
a processor; a data acquisition module coupled to the processor to,
obtain transaction data associated with one or more products being purchased by a customer;
generate a customer household graph based on the transaction data, wherein the customer household graph comprises a customer household node associated with the customer, a product superset node associated with the customer household node, and one or more product nodes associated with the product superset node, wherein each of the one or more product nodes represents a product from amongst the one or more products; and
an inference module coupled to the processor to,
determine at least one product metadata node associated with each of the one or more product nodes based on the transaction data, wherein the at least one product metadata node represents product metadata associated with the product;
associate at least one user node with the customer household node based on the at least one product metadata node, wherein the at least one user node indicates a user of a product corresponding to the at least product metadata node associated with the product node;
determine a user group node associated with the customer household node based on the at least one user node, wherein the user group node indicates a user group comprising one or more users associated with the customer; and
a recommendation module coupled to the processor to ascertain one or more recommendations to be provided to the customer.

2 . The recommendation engine as claimed in claim 1 , wherein the inference module further is to associate a confidence value with the at least one product metadata node, wherein the confidence value indicates accuracy of identification of the product metadata.

3 . The recommendation engine as claimed in claim 1 , wherein the data acquisition module further is to,
generate a spatial node indicative of a location of the purchase; and generate a temporal node indicative of a time of the purchase.

4 . The recommendation engine as claimed in claim 1 , wherein the inference module further is to perform a first inference based on the at least one product metadata node and the transaction data.

5 . The recommendation engine as claimed in claim 1 , wherein the recommendation engine further comprises a clustering module coupled to the processor to cluster the user group node into at least one cluster based on one or more predetermined clustering rules.

6 . The recommendation engine as claimed in claim 1 , wherein the recommendation module further is to ascertain the one or more recommendations based on at least one of the user node, the user group node, the transaction data, one or more clusters, and a recommendation training dataset.

7 . A method for providing recommendations for customers, the method comprising:
obtaining transaction data associated with one or more products being purchased by a customer; generating a customer household graph based on the transaction data, wherein the customer household graph comprises a customer household node associated with the customer, a product superset node, and one or more product nodes associated with the product superset node, wherein each of the one or more product nodes represents a product from amongst the one or more products; determining at least one product metadata node associated with each of the one or more product nodes based on the transaction data, wherein the at least one product metadata node represents product metadata associated with the product; associating at least one user node with the customer household node based on the at least one product metadata node, wherein the at least one user node indicates a user of a product corresponding to the at least product metadata node associated with the product node; determining a user group node associated with the customer household node based on the at least one user node, wherein the user group node indicates a user group comprising one or more users associated with the customer; and ascertaining one or more recommendations to be provided to the user.

8 . The method as claimed in claim 7 , wherein the transaction data comprises product data, product metadata, spatial data, and temporal data, wherein the product data and the product metadata comprises information associated with the one or more products, and wherein the spatial data comprises information indicative of a location where the customer is purchasing the product, and wherein the temporal data comprises information indicative of a date and a time of purchase of the one or more products.

9 . The method as claimed in claim 7 , wherein the customer household graph further comprises a relation between each of the one or more product nodes and the product superset node.

10 . The method as claimed in claim 7 , wherein a confidence value is associated with the at least one product metadata node, wherein the confidence value indicates accuracy of identification of the product metadata.

11 . The method as claimed in claim 7 , wherein the associating further comprises performing a first inference based on the at least one product metadata node and the transaction data for obtaining the at least one user node.

12 . The method as claimed in claim 7 , wherein the method further comprises clustering the user group node into at least one cluster based on one or more predetermined clustering rules.

13 . The method as claimed in claim 7 , wherein the one or more recommendations are ascertained based on at least one of the user node, the user group node, the transaction data, one or more clusters, and a recommendation training dataset.

14 . The method as claimed in claim 7 , wherein a confidence value is associated with the at least one user node, wherein the confidence value indicates accuracy of identification of the user.

15 . The method as claimed in claim 7 , wherein a confidence value is associated with the user group node, wherein the confidence value indicates accuracy of determination of the user group.

16 . The method as claimed in claim 7 , wherein the method further comprises:
generating a spatial node indicative of a location of purchase of the one or more products; and generating a temporal node indicative of a time of purchase of the one or more products.

17 . A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising:
obtaining transaction data associated with one or more products being purchased by a customer; generating a customer household graph based on the transaction data, wherein the customer household graph comprises a customer household node associated with the customer, a product superset node, and one or more product nodes associated with the product superset node, wherein each of the one or more product nodes represents a product from amongst the one or more products; determining at least one product metadata node associated with each of the one or more product nodes based on the transaction data, wherein the at least one product metadata node represents product metadata associated with the product; associating at least one user node with the customer household node based on the at least one product metadata node, wherein the at least one user node indicates a user of a product corresponding to the at least product metadata node associated with the product node; determining a user group node associated with the customer household node based on the at least one user node, wherein the user group node

Assignees

• Tata Consultancy Services Ltd

Inventors

• Chacko Viju
• Ramaswamy Satyanarayanan
• Sarkar Shampa

Classifications

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title