SYSTEM AND METHOD FOR AUTOMATIC PRODUCT MATCHING

• US 20140136549A1
• Filed: 03/15/2013
• Published: 05/15/2014
• Est. Priority Date: 11/14/2012
• Status: Active Grant

• Current Assignee: Home Depot Product Authority, LLC (The Home Depot, Inc.)
• Original Assignee: Homer TLC Incorporated (The Home Depot, Inc.)
• Inventors: Surya, Siva Kalyana Pavan Kumar Mallapragada Naga, Barcelo, Carlos
• Granted Patent: US 10,664,534 B2
• Time in Patent Office: Days
• US Class Current: 707/749
• CPC Class Codes: G06F 16/9532 Query formulation
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A source product and a competitor product are automatically matched by computing similarity scores between the products. An attribute of a source product is extracted from information received from a user. The extracted attribute is then used to generate a search term. A search for competitor product information is performed using the search term and a similarity score between the source product and the competitor product is calculated. In one implementation, the similarity score is calculated by generating a first value pair for the source product and the competitor product, and for the first value pair, (1) assigning a value to a degree of similarity between the source product and the competitor product with respect to a common feature, and (2) finding the product of the assigned value and a weight value assigned to the common feature.

Claims

1. A method for automatically matching products, the method comprising:

extracting at least one attribute of a source product from source product information received from a user;

generating a search term from the at least one extracted attribute;

using the search term to search for competitor product information; and

computing a similarity score between the source product information and the competitor product information, wherein computing the similarity score comprises;

generating a first value pair for the source product and a first competitor product, and for the first value pair;

assigning a value to a degree of similarity between the source product and the first competitor product with respect to a first common feature; and

finding the product of the assigned value and a weight value assigned to the first common feature.

2. The method of claim 1, wherein computing the similarity score further comprises:

for the first value pair;

assigning a value to a degree of similarity between the source product and the first competitor product with respect to a second common feature;

finding the product of the assigned value and a weight value assigned to the second common feature; and

summing the product for the first common feature and the product for the second common feature.

3. The method of claim 1, wherein computing the similarity score further comprises:

generating a second value pair for the source product and a second competitor product, and for the second value pair;

assigning a value to a degree of similarity between the source product and the second competitor product with respect to a first common feature; and

finding the product of the assigned value and a weight value assigned to the first common feature.

4. The method of claim 1, wherein the weight value assigned to the first common feature is computed according to a logistic regression model.

5. The method of claim 1, wherein extracting the at least one attribute of a source product comprises:

identifying at least one tag in the source product information.

6. The method of claim 5, further comprising prioritizing identified tags.

7. The method of claim 1, wherein generating the search term from the at least one extracted attribute comprises:

prioritizing extracted attributes; and

generating the search term according to the priority.

8. The method of claim 7, wherein generating the search term according to the priority comprises:

deleting low priority extracted attributes.

9. The method of claim 1, wherein generating a first value pair for the source product and a first competitor product comprises:

converting the first value pair into a vector, the vector representing similarity between the information relating to the source produce and the first competitor product.

10. The method of claim 6, further comprising:

filtering out at least one value pair that violates a must match constraint.

11. A system for automatically matching products, the system comprising:

a memory; and

a processor coupled to said memory, said processor configured to;

extracting at least one attribute of a source product from source product information received from a user;

generate a search term from the at least one extracted attribute;

use the search term to search for competitor product information; and

compute a similarity score between the source product information and the competitor product information, wherein computing the similarity score comprises;

generate a first value pair for the source product and a first competitor product, and for the first value pair;

assign a value to a degree of similarity between the source product and the first competitor product with respect to a first common feature; and

find the product of the assigned value and a weight value assigned to the first common feature.

12. The system of claim 11, wherein the processor is further configured to:

for the first value pair;

assign a value to a degree of similarity between the source product and the first competitor product with respect to a second common feature;

find the product of the assigned value and a weight value assigned to the second common feature; and

sum the product for the first common feature and the product for the second common feature.

13. The system of claim 11, wherein the processor is further configured to:

generate a second value pair for the source product and a second competitor product, and for the second value pair;

assign a value to a degree of similarity between the source product and the second competitor product with respect to a first common feature; and

find the product of the assigned value and a weight value assigned to the first common feature.

14. The system of claim 11, wherein the weight value assigned to the first common feature is computed according to a logistic regression model.

15. The system of claim 11, wherein the processor is further configured to:

identify at least one tag in the source product information.

16. The system of claim 15, wherein the processor is further configured to:

prioritize identified tags.

17. The system of claim 11, wherein the processor is further configured to:

prioritize extracted attributes; and

generate the search term according to the priority.

18. The system of claim 17, wherein the processor is further configured to:

delete low priority extracted attributes.

19. The system of claim 11, wherein the processor is further configured to:

convert the first value pair into a vector, the vector representing similarity between the information relating to the source produce and the first competitor product.

20. The system of claim 16, wherein the processor is further configured to:

filter out at least one value pair that violates a must match constraint.