Bayesian product recommendation engine

• US 20040059626A1
• Filed: 09/23/2002
• Published: 03/25/2004
• Est. Priority Date: 09/23/2002
• Status: Active Grant

• Current Assignee: General Motors LLC (General Motors Company)
• Original Assignee: General Motors Corporation (Motors Liquidation Co. GUC Trust)
• Inventors: Smallwood, Richard D.
• Granted Patent: US 8,255,263 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

The invention provides a method of generating a recommendation for a product type. A plurality of product attributes associated with the product type is provided. A sampled set of consumer values is received. At least one user preference corresponding to the product attributes is received. A plurality of consumer values based on the at least one user preference and the sample set of consumer values is calculated, and at least one product recommendation is determined based on the calculated consumer values.

Claims

1. A method of generating a product recommendation for a product type, comprising:

providing a plurality of product attributes associated with the product type;

receiving a sampled set of consumer values;

receiving at least one user preference corresponding to the product attributes;

calculating a plurality of consumer values based on the at least one user preference and the sampled set of consumer values; and

determining at least one product recommendation based on the calculated consumer values.

2. The method of claim 1 wherein the product type comprises mobile vehicles.

3. The method of claim 1 wherein the product type is selected from the group consisting of digital cameras, personal computers, personal digital assistants, computer printers, cell phones, televisions, cameras, entertainment systems, power tools, MP3/CD players, DVD players, consumer electronic products, consumer appliances, consumer goods, consumer services, office equipment, business software, credit cards, home mortgages, car loans, health insurance, restaurants, telephone services, hotel rooms, airplane tickets, vacation packages, a consumer-valued product, and a consumer-valued service.

4. The method of claim 1 wherein the plurality of product attributes includes at least one attribute level associated with each product attribute.

5. The method of claim 1 wherein the sampled set of consumer values includes a set of measured product attribute values from a set of consumers.

6. The method of claim 1 wherein the user preference is received via a web interface.

7. The method of claim 1 wherein calculating a plurality of consumer values comprises determining a likelihood function based on the at least one user preference, calculating an initial Gaussian probability density function based on the sampled set of consumer values, calculating a posterior Gaussian probability density function based on the likelihood function and the initial Gaussian probability density function, and calculating a plurality of consumer values based on the posterior Gaussian probability density function.

8. The method of claim 7 wherein the likelihood function is characterized by a linear form and an additive Gaussian noise function with a variance based on the sampled set of consumer values.

9. The method of claim 7 wherein the posterior Gaussian probability density function is characterized by a mean vector and a precision matrix.

10. The method of claim 1 wherein determining at least one product recommendation comprises computing a total value for each of a set of products in a product-definition database, sorting the total values to produce an ordered list of total values, and associating a product in the product-definition database with the ordered list of total values.

11. The method of claim 1 further comprising:

displaying the at least one product recommendation based on the calculated consumer values.

12. The method of claim 1 further comprising:

receiving a product-recommendation request; and

displaying the at least one product recommendation based on the product-recommendation request and the calculated consumer values.

13. The method of claim 1 further comprising:

generating a product type market study based on the plurality of calculated consumer values.

14. A computer usable medium including a program to generate a product recommendation for a product type, comprising:

computer program code to providing a plurality of product attributes associated with the product type;

computer program code to receive a sampled set of consumer values;

computer program code to receive at least one user preference corresponding to the product attributes;

computer program code to calculate a plurality of consumer values based on the at least one user preference and the sampled set of consumer values; and

computer program code to determine at least one product recommendation based on the calculated consumer values.

15. The computer usable medium of claim 14, further comprising:

computer program code to display the at least one product recommendation based on the calculated consumer values.

16. The computer usable medium of claim 14, further comprising:

computer program code to receive a product-recommendation request; and

computer program code to display the at least one product recommendation based on the product-recommendation request and the calculated consumer values.

17. The computer usable medium of claim 14, further comprising:

computer program code to generate a product type market study based on the plurality of calculated consumer values.

18. A system to generate a product recommendation for a product type, comprising:

means for providing a plurality of product attributes associated with the product type;

means for receiving a sampled set of consumer values;

means for receiving at least one user preference corresponding to the product attributes;

means for calculating a plurality of consumer values based on the at least one user preference and the sampled set of consumer values; and

means for determining at least one product recommendation based on the calculated consumer values.

19. The system of claim 18 further comprising:

means for displaying the at least one product recommendation based on the calculated consumer values.

20. The system of claim 18 further comprising:

means for receiving a product-recommendation request; and

means for displaying the at least one product recommendation based on the product-recommendation request and the calculated consumer values.

21. The system of claim 18 further comprising:

means for generating a product type market study based on the plurality of calculated consumer values.