Systems and methods for variant item recommendation

Patent metadata

Field	Value
Publication number	US-2024257205-A1
Application number	US-202318104072-A
Country	US
Kind code	A1
Filing date	Jan 31, 2023
Priority date	Jan 31, 2023
Publication date	Aug 1, 2024
Grant date	—

Abstract

Systems and methods of generating interfaces including variant item recommendations are disclosed. A request for an interface and a set of candidate items selected from an item catalog are received. At least one of the candidate items is representative of two or more variant items. A variant score is determined for each variant item related to the at least one of the candidate items and a set of recommended items is generated by independently ranking each item in the set of candidate items and each of the two or more variant items. The set of recommended items is generated by a variant-aware ranking model configured to receive the variant score for each variant item and based on the variant score and a relevancy score for each variant item. The interface including the set of recommended items is generated and transmitted to a system that generated the request for the interface.

First claim

What is claimed is:

1 . A system, comprising:
a non-transitory memory; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to:
receive a request for an interface;
receive a set of candidate items selected from an item catalog, wherein at least one of the candidate items is representative of two or more variant items in the item catalog;
determine a variant score for each variant item related to the at least one of the candidate items;
generate a set of recommended items by independently ranking each item in the set of candidate items and each of the two or more variant items, wherein the set of recommended items is generated by a variant-aware ranking model configured to receive the variant score for each variant item, and wherein the set of recommended items is generated based on the variant score and a relevancy score for each variant item;
generate the interface including the set of recommended items; and
transmit the interface to a system that generated the request for the interface.

2 . The system of claim 1 , wherein the variant score is determined based on historical sales of the variant item and interaction data for the variant item.

3 . The system of claim 1 , wherein the variant-aware ranking model is configured to generate the relevancy score.

4 . The system of claim 1 , wherein the request for the interface includes a contextual identifier, and wherein the relevancy score is generated based on a correspondence between each variant item and the contextual identifier.

5 . The system of claim 1 , wherein the processor is further configured to read the set of instructions to, prior to generating the interface, filter the set of recommended items based on current availability of each variant item.

6 . The system of claim 1 , wherein the variant score for each variant item is precalculated by a batch process.

7 . The system of claim 1 , wherein the processor is further configured to read the set of instructions to:
receive feedback data indicative of one or more interactions with the interface; iteratively train an updated variant-aware ranking model based at least in part on the feedback data; and store the updated variant-aware ranking model in a model store database.

8 . The system of claim 1 , wherein the set of recommended items includes each of the two or more variant items.

9 . A computer-implemented method, comprising:
receiving a request for an interface; receiving a set of candidate items selected from an item catalog, wherein at least one of the candidate items is representative of two or more variant items in the item catalog; determining a variant score for each variant item related to the at least one of the candidate items; generating a set of recommended items by independently ranking each item in the set of candidate items and each of the two or more variant items, wherein the set of recommended items is generated by a variant-aware ranking model configured to receive the variant score for each variant item, and wherein the set of recommended items is generated based on a relevancy score for each variant item; generating the interface including the set of recommended items; and transmitting the interface to a system that generated the request for the interface.

10 . The computer-implemented method of claim 9 , wherein the variant score is determined based on historical sales of the variant item and interaction data for the variant item.

11 . The computer-implemented method of claim 9 , wherein the variant-aware ranking model is configured to generate the relevancy score.

12 . The computer-implemented method of claim 9 , wherein the request for the interface includes a contextual identifier, and wherein the relevancy score is generated based on a correspondence between each variant item and the contextual identifier.

13 . The computer-implemented method of claim 9 , comprising, prior to generating the interface, filtering the set of recommended items based on current availability of each variant item.

14 . The computer-implemented method of claim 9 , wherein the variant score for each variant item is precalculated by a batch process.

15 . The computer-implemented method of claim 9 , comprising:
receiving feedback data indicative of one or more interactions with the interface; iteratively training an updated variant-aware ranking model based at least in part on the feedback data; and storing the updated variant-aware ranking model in a model store database.

16 . The computer-implemented method of claim 9 , wherein the set of recommended items includes each of the two or more variant items.

17 . A non-transitory computer-readable medium having instructions stored thereon which, when executed by a processor, cause a device to perform operations comprising:
receiving a request for an interface; receiving a set of candidate items selected from an item catalog, wherein at least one of the candidate items is representative of two or more variant items in the item catalog; determining a variant score for each variant item related to the at least one of the candidate items; generating, by a variant-aware ranking model, a relevancy score for each variant item; generating a set of recommended items by independently ranking each item in the set of candidate items and each of the two or more variant items, wherein the set of recommended items is generated by a variant-aware ranking model configured to receive the variant score for each variant item, and wherein the set of recommended items is generated based on the variant score and the relevancy score for each variant item; generating the interface including the set of recommended items; and transmitting the interface to a system that generated the request for the interface.

18 . The non-transitory computer-readable medium of claim 17 , wherein the variant score is determined based on historical sales of the variant item and interaction data for the variant item.

19 . The non-transitory computer-readable medium of claim 17 , wherein the request for the interface includes a contextual identifier, and wherein the relevancy score is generated based on a correspondence between each variant item and the contextual identifier.

20 . The non-transitory computer-readable medium of claim 17 , wherein the instructions further cause the device to perform operations comprising, prior to generating the interface, filtering the set of recommended items based on current availability of each variant item.

Assignees

• Walmart Apollo Llc

Inventors

• Upreti Kritika
• Xue Yanbing
• Ananth Rithvik Reddy
• Patel Mohit Prakash
• Korlimarla Jayanth
• Wen Musen

Classifications

• G06Q30/0631

  Recommending goods or services · CPC title

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title

• G06Q30/0629 Primary

  by pre-processing results, e.g. ranking or ordering results · CPC title