Computer processes for identifying related items and generating personalized item recommendations

• US 7,945,475 B2
• Filed: 08/20/2007
• Issued: 05/17/2011
• Est. Priority Date: 09/18/1998
• Status: Expired due to Fees

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon.com, Inc.
• Inventors: Jacobi, Jennifer A., Benson, Eric A., Linden, Gregory D.
• Primary Examiner(s): CHAMPAGNE, DONALD
• Application Number:
  US11/841,938
• Publication Number:
  US 20080040239A1
• Time in Patent Office: 1,366 Days
• Field of Search: 705/14.53, 705/14.67, 705/14.73
• US Class Current: 705/14.53
• 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.

Claims

1. A computer-implemented method of selecting items to recommend to a user, the method comprising:

identifying a plurality of items selected by a user from an electronic repository of items;

for each of the plurality of items selected by the user, looking up, from an item-to-item mapping structure, a respective set of related items and, for each related item, a respective data value representing a degree to which the related item is related to the respective item selected by the user, each data value being specific to a particular item pair;

generating a combined list of related items that comprises the plurality of sets of related items looked up from the item-to-item mapping structure;

sorting the combined list of related items based at least partly on the looked up data values associated with the related items; and

selecting, from the sorted, combined list of related items, a subset of items to recommend to the user;

said method performed automatically by a computer system that comprises one or more computers.

2. The method of claim 1, wherein the method comprises sorting the combined list such that the position of a related item in the sorted list depends on whether the related item is related to more than one of the items selected by the user.

3. The method of claim 1, wherein the data values reflect purchase-based relationships between particular items as assessed by analyzing purchase histories of a plurality of users.

4. The method of claim 1, wherein the data values reflect item relationships determined based on item viewing activities of a plurality of users.

5. The method of claim 4, wherein the items include videos.

6. The method of claim 1, further comprising generating said data values based at least partly on recorded item viewing activities of a plurality of users.

7. The method of claim 1, wherein generating the combined list of related items comprises combining the data values of like related items.

8. The method of claim 1, wherein generating the combined list of related items comprises weighting some of said sets of related items more heavily than others, wherein weighting a set of related items relatively heavily increases a likelihood that items will be selected from that set of related items to recommend to the user.

9. The method of claim 8, wherein the method comprises, for a set of related items corresponding to an item explicitly rated by the user, weighting the set of related items by an amount that depends on the user'"'"'s explicit rating of said item.

10. The method of claim 8, wherein the method comprises, for a set of related items corresponding to a first item selected by the user, weighting the set of related items by an amount that depends on how recently the user selected said first item.

11. The method of claim 1, wherein identifying a plurality of items selected by the user comprises identifying said plurality of items based on item viewing activities of the user.

12. The method of claim 11, wherein the items selected by the user are videos selected by the user for viewing.

13. The method of claim 1, wherein identifying a plurality of items selected by the user comprises identifying said plurality of items based at least partly on explicit item ratings assigned by the user to particular items.

14. The method of claim 1, wherein the plurality of items selected by the user consists of items represented in an electronic shopping cart of the user, and the method further comprises outputting a shopping cart page that lists both (1) the items represented in the electronic shopping cart and (2) the subset of items.

15. A system for selecting items to recommend to a user, the system comprising:

a computer system comprising one or more physical servers, said computer system programmed to at least;

identify a plurality of items selected by a user from an electronic repository of items;

for each of the plurality of items selected by the user, look up, from an item-to-item mapping structure, a respective set of related items and, for each related item, a respective data value representing a degree to which the related item is related to the respective item selected by the user, each data value being specific to a particular item pair;

generate a combined list of related items that comprises the plurality of sets of related items looked up from the item-to-item mapping structure;

sort the combined list of related items based at least partly on the looked up data values associated with the related items; and

select, from the sorted, combined list of related items, a subset of items to recommend to the user.

16. The system of claim 15, wherein the computer system is programmed to sort the combined list such that the position of a related item in the sorted list depends on whether the related item is related to more than one of the items selected by the user.

17. The system of claim 15, wherein the data values reflect purchase-based relationships between particular items as assessed by analyzing purchase histories of a plurality of users.

18. The system of claim 15, wherein the data values reflect item relationships determined based on item viewing activities of a plurality of users.

19. The system of claim 18, wherein the items include videos.

20. The system of claim 15, wherein the computer system is programmed to generate said data values based at least partly on recorded item viewing activities of a plurality of users.

21. The system of claim 15, wherein the computer system is programmed to generate the combined list of related items by a method that comprises combining the data values of like related items.

22. The system of claim 15, wherein the computer system, in generating the combined list of related items, is programmed to weight some of said sets of related items more heavily than others, wherein weighting a set of related items relatively heavily increases a likelihood that items will be selected from that set of related items to recommend to the user.

23. The system of claim 22, wherein the method comprises, for a set of related items corresponding to an item explicitly rated by the user, weighting the set of related items by an amount that depends on the user'"'"'s explicit rating of said item.

24. The system of claim 22, wherein the computer system is programmed to weight a first set of related items, said first set corresponding to a first item selected by the user, by an amount that depends on how recently the user selected said first item.

25. The system of claim 15, wherein the computer system is programmed to identify the plurality of items selected by the user based at least partly on item viewing activities of the user.

26. The system of claim 25, wherein the items selected by the user are videos selected by the user for viewing.

27. The system of claim 15, wherein the computer system is programmed to identify the plurality of items selected by the user based at least partly on explicit item ratings assigned by the user to particular items.

28. The system of claim 15, wherein the plurality of items selected by the user consists of items represented in an electronic shopping cart of the user, and the computer system is configured to output a shopping cart page that lists both (1) the items represented in the electronic shopping cart and (2) the subset of items.

29. Physical computer storage that stores executable code that directs a computing system to implement a method that comprises:

identifying a plurality of videos selected by a user from an electronic repository of videos;

for each of the plurality of videos selected by the user, looking up, from a video-to-video mapping structure, a respective set of related videos and, for each related video, a respective data value representing a degree to which the related video is related to the respective video selected by the user, each data value being specific to a particular video pair;

generating a combined list of related videos that comprises the plurality of sets of related videos looked up from the video-to-video mapping structure;

sorting the combined list of related videos based at least partly on the looked up data values associated with the related videos; and

selecting, from the sorted, combined list of related videos, a subset of videos to recommend to the user.

30. The physical computer storage of claim 29, wherein the executable code directs the computing system to generate the video-to-video mapping structure based at least partly on recorded video viewing activities of a plurality of users.

31. The physical computer storage of claim 29, wherein the executable code directs the computing system to identify the plurality of videos based on a record of videos selected by the user for viewing.

32. The physical computer storage of claim 29, in combination with said computing system, wherein the computing system is programmed with said executable code to implement said method.