Systems, methods, and devices for measuring similarity of and generating recommendations for unique items

• US 9,710,843 B2
• Filed: 03/21/2016
• Issued: 07/18/2017
• Est. Priority Date: 03/07/2013
• Status: Active Grant

• Current Assignee: Vast.com, Inc. (Vroom, Inc.)
• Original Assignee: Vast.com, Inc. (Vroom, Inc.)
• Inventors: Franke, David Wayne, Levy, Joshua Howard
• Primary Examiner(s): CHEN, ALAN S
• Application Number:
  US15/076,468
• Publication Number:
  US 20160343058A1
• Time in Patent Office: 484 Days
• Field of Search: None
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

The disclosure herein provides methods, systems, and devices for measuring similarity of and generating recommendations for unique items. A recommendation system for generating recommendations of alternative unique items comprises an items information database, a penalty computation engine, a recommendation compilation engine, and one or more computers, wherein the penalty computation engine comprises a customizations filter, a condition filter, and a dissimilarity penalty calculator.

Claims

1. A computer-based recommendation system for generating recommendations of unique items, the recommendation system comprising:

one or more computer readable storage devices configured to store;

a plurality of computer executable instructions;

an items information database containing data relating to a plurality of unique items;

one or more hardware computer processors in communication with the one or more computer readable storage devices and configured to execute the plurality of computer executable instructions in order to cause the computer system to;

receive an input from a user that comprises user-expressed preferences associated with the plurality of unique items;

calculate a customization score for each unique item in the plurality of unique items, the customization score at least partially based on at least one customization attribute associated with that unique item;

calculate a condition score for each unique item in the plurality of unique items, the condition score at least partially based on at least one condition attribute associated with that unique item;

generate a dissimilarity penalty for each unique item in the plurality of unique items by combining the customization score and the condition score for that unique item, the dissimilarity penalty at least partially generated based on a magnitude of dissimilarity between the unique item and the user-expressed preferences; and

generate a recommendation of unique items by ranking at least a portion of the plurality of the unique items based at least partially on the calculated dissimilarity penalties.

2. The computer-based recommendation system of claim 1, wherein the user-expressed preferences comprise filters that the user has applied, wherein the filters are configured to filter the plurality of unique items for display by a display interface, and wherein the one or more hardware computer processors are further configured to execute the plurality of computer executable instructions in order to:

generate user interface data for rendering the display interface, wherein the display interface displays the filtered unique items based on the recommendation of unique items.

3. The computer-based recommendation system of claim 1, wherein the unique items comprise one of the following types of items:

used automobiles, existing homes, real estate, household goods, customized electronics, customized goods.

4. The computer-based recommendation system of claim 1, wherein the user-expressed preferences comprise a plurality of attributes, the plurality of attributes comprising at least one customization attribute and at least one condition attribute.

5. The computer-based recommendation system of claim 1, wherein the unique items comprise used automobiles and the user-expressed preferences comprise a plurality of attributes associated with used automobiles.

6. The computer-based recommendation system of claim 1, wherein the at least one customization attribute describes at least one of the following:

an engine size, a type of material used for an interior of an automobile, a color of an automobile.

7. The computer-based recommendation system of claim 1, wherein the at least one condition attribute of the selected and alternative items describes at least one of the following:

a number of miles an automobile has been driven, whether an automobile'"'"'s title is clean, whether an automobile has been in an accident.

8. The computer-based recommendation system of claim 1, wherein the calculation of the customization score comprises a Mahalanobis distance calculation.

9. The computer-based recommendation system of claim 1, wherein the customization score comprises an estimated price impact.

10. The computer-based recommendation system of claim 1, wherein calculating the condition score comprises a Mahalanobis distance calculation.

11. The computer-based recommendation system of claim 1, wherein the condition score comprises an estimated price impact.

12. The computer-based recommendation system of claim 1, wherein the items information database is configured to store data relating to at least 1,000 unique items.

13. The computer-based recommendation system of claim 1, wherein the plurality of unique items comprises at least 100 unique items, and generating the recommendation of unique items occurs in real time.

14. The computer-based recommendation system of claim 1, wherein the condition score for each unique item represents an estimated preference impact based on at least one status attribute associated with that unique item.

15. The computer-based recommendation system of claim 14, wherein the at least one status attribute describes at least one of the following:

a listing price, a geographic location, a type of seller.

16. The computer-based recommendation system of claim 1, wherein the one or more hardware computer processors are further configured to execute the plurality of computer executable instructions in order to cause the computer system to:

calculate a probability score for each unique item in the plurality of unique items, the probability score at least partially based on a probability that the user will be interested in that unique item based on the user-expressed preferences; and

wherein the dissimilarity penalty for each unique item is at least partially generated based on combining that unique item'"'"'s probability score, customization score and condition score.

17. The computer-based recommendation system of claim 1, wherein the user-expressed preferences comprise a search query.

18. The computer-based recommendation system of claim 1, wherein the one or more hardware computer processors are further configured to execute the plurality of computer executable instructions in order to cause the computer system to access the items information database.

19. A computer-implemented method for generating recommendations of unique items, the computer-implemented method comprising:

receiving an input from a user that comprises user-expressed preferences associated with a plurality of unique items;

calculating, using a computer system, a customization score for each unique item in the plurality of unique items, the customization score at least partially based on at least one customization attribute associated with that unique item;

calculating, using the computer system, a condition score for each unique item in the plurality of unique items, the condition score at least partially based on at least one condition attribute associated with that unique item;

generating, using the computer system, a dissimilarity penalty for each unique item in the plurality of unique items by combining the customization score and the condition score for that unique item, the dissimilarity penalty at least partially generated based on a magnitude of dissimilarity between the unique item and the user-expressed preferences; and

generating, using the computer system, a recommendation of unique items by ranking at least a portion of the plurality of the unique items based at least partially on the calculated dissimilarity penalties;

wherein the computer system comprises a computer processor and electronic memory.

20. The computer-implemented method of claim 19, wherein the unique items comprise one of the following types of items:

used automobiles, existing homes, real estate, household goods, customized electronics, customized goods.