Recommendation system

• US 20060195362A1
• Filed: 03/31/2006
• Published: 08/31/2006
• Est. Priority Date: 09/18/1998
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Eric A. Benson, Gregory D. Linden, Jennifer A. Jacobi
• Inventors: Jacobi, Jennifer A., Benson, Eric A., Linden, Gregory D.
• Granted Patent: US 7,908,183 B2
• Time in Patent Office: Days
• US Class Current: 705/343
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: H04L 51/02 using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A computer-implemented service analyzes purchase histories and/or other types of behavioral data of users on an aggregated basis to detect and quantify associations between particular items represented in an electronic catalog. The detected associations are stored in a mapping structure that maps items to related items, and is used to recommend items to users of the electronic catalog. The items may include products and/or categories of products.

Claims

1. A computer system, comprising:

a first component configured to generate, for each of a plurality of item pairs, a respective data value representing a degree to which the items of the pair are related to each other, said data value being dependent upon how frequently the items of the pair have co-occurred in purchase histories of a plurality of users;

a data repository which stores at least some of the data values in association with the items to which they correspond; and

a second component configured to receive an input list of items selected by a user, and to use the associated data values stored in the data repository to generate an output list of items that are predicted to be of interest to the user, said input and output lists each including multiple items.

2. The computer system of claim 1, wherein the data repository comprises a mapping structure that associates related items.

3. The computer system of claim 2, wherein the first component uses the data values to select item pairs to include in the mapping structure.

4. The computer system of claim 1, wherein the first component is configured to exclude, from the data repository, data values that do not satisfy a selected condition.

5. The computer system of claim 1, wherein the input list includes at least one purchased item that was purchased by the user on a purchase date, and the second component is configured to take the purchase date into consideration in determining how much weight to give to the purchased item in generating the output list of items.

6. The computer system of claim 1, wherein the input list includes at least one item to which the user has explicitly assigned a rating, and the second component is configured to take the rating into consideration in generating the output list of items.

7. The computer system of claim 1, wherein the first component is configured to additionally take item viewing histories of users into consideration in generating the data values.

8. The computer system of claim 1, wherein the second component generates the input list of items, at least in part, by identifying items viewed by the user during browsing of an electronic catalog.

9. The computer system of claim 1, wherein the items are products represented in an electronic catalog.

10. The computer system of claim 1, wherein the items are categories of products represented in the electronic catalog.

11. A computer system, comprising:

a first component configured to analyze purchases of a plurality of users of an electronic catalog of items to detect purchase-based relationships between particular categories of said items, and to generate, based at least in part on the purchase-based relationships, a mapping structure that maps particular categories of items to related categories of items; and

a second component that uses the mapping structure to select particular categories of items to recommend to users of the electronic catalog.

12. The computer system of claim 11, wherein the first component is configured to quantify a degree of relationship between a first category of items and a second category of items based, at least in part, on how frequently users who make a purchase from the first category also make a purchase from the second category.

13. The computer system of claim 11, wherein first component is configured to generate data values representing degrees of relationship between particular item categories, and to use the data values to select particular item categories to associate with each other in the mapping structure.

14. The computer system of claim 11, wherein the first component is configured to store selected ones of said data values in the mapping structure in association with the item categories to which they correspond.

15. The computer system of claim 11, wherein the first component is additionally configured to analyze catalog viewing histories of users.

16. A computer-implemented method comprising:

programmatically analyzing purchases of a plurality of users of an electronic catalog of items to detect purchase-based relationships between particular categories of said items;

generating, based at least in part on said purchase-based relationships, a mapping structure that maps particular categories of items to related categories of items; and

using said mapping structure to select particular categories of items to recommend to users of the electronic catalog.

17. The method of claim 16, wherein the method comprises quantifying a degree of relationship between a first category of items and a second category of items based, at least in part, on how frequently users who make a purchase from the first category also make a purchase from the second category.

18. The method of claim 16, further comprising generating data values reflective of the purchase-based relationships between particular item categories, and using the data values to select particular item categories to associate in the mapping structure.