PRODUCT RECOMMENDATIONS AND WEIGHTING OPTIMIZATION SYSTEMS

• US 20120197751A1
• Filed: 01/24/2012
• Published: 08/02/2012
• Est. Priority Date: 01/27/2011
• Status: Abandoned Application

• Current Assignee: Electronic Entertainment Design And Research
• Original Assignee: Electronic Entertainment Design And Research
• Inventors: Zatkin, Geoffrey C., Short, Gregory T., Spence, Theodore, Divnich, Jesse
• Application Number:
  US13/357,450
• Publication Number:
  US 20120197751A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A product recommendation ecosystem is presented. A rules engine seeks to discover one or more relationships among cross-brand product categories based on non-transaction correlations. The rules engine constructs a generic rules-set based on the universal relationships. The rules-set is sent to a recommendation engine, possibly a subscriber to the services offered by the rules engine, and the rules-set configure the recommendation engine to generate one or more cross-brand product recommendations. The recommendation engine customizes the rules-set according to location-specific information possibly comprising consumer parameters, product parameters, vendor parameters, or other local information.

Claims

1. A recommendation system, the system comprising:

a product database storing product information relating to products across brand classifications, the product information including product attributes associated with the products; and

a rules engine communicatively coupled with the product database and configured to;

discover universal relationships based on non-transaction correlations among the product attributes of products spanning across brand classifications;

create a cross-brand rules-set that configures a recommendation engine to generate product recommendations in a first brand classification based on products in a second different brand classification based on at least some of universal relationships; and

configure the recommendation engine to operate according to the rules-set.

2. The system of claim 1, wherein the rules engine is further configured create the cross-brand rules-set as a serialized instruction set.

3. The system of claim 1, wherein the rules engine is further configured to discover weighting factors relating the first and the second brand classifications based on the universal relationships.

4. The system of claim 3, wherein the rules engine is further configured to create the cross-brand rules-set based on the weighting factors.

5. The system of claim 3, wherein at least one of the weighting factors is a range within a weighting range.

6. The system of claim 5, wherein the weighting range comprises a normalized low end greater than zero.

7. The system of claim 1, wherein the rules engine is further configured to create the cross-brand rules-set based on randomized product recommendation rules.

8. The system of claim 1, wherein the first and second brand classifications include at least two different ones of the following classifications:

genre, product type, media type, supply chains, celebrity, vendor, publisher, and franchise.

9. The system of claim 1, further comprising a kiosk configured as the recommendation engine.

10. The system of claim 1, further comprising the recommendation engine wherein the recommendation engine is configured to couple with a local product database local to the recommendation engine.

11. The system of claim 10, wherein the cross-brand rules-set configures the recommendation engine to construct product queries according to the rules-set and targeting the local product database.

12. The system of claim 10, wherein the cross-brand rules-set configures the recommendation engine to select products as recommended products from a result set obtained from the local product database.

13. The system of claim 10, wherein the cross-brand rules-set comprises instructions having consumer variables.

14. The system of claim 13, wherein the recommendation engine is configured to populate the consumer variables a function of consumer information.

15. The system of claim 10, wherein the cross-brand rules-set comprises instructions having product variables.

16. The system of claim 15, wherein the recommendation engine is configured to populate the product variables brand parameters a function of product information in the local product database.