Systems and methods for item-specific keyword recommendation

Patent metadata

Field	Value
Publication number	US-2024256584-A1
Application number	US-202318104083-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 item-specific keyword recommendation are disclosed. An item data structure including an item title is received and at least one item embedding is generated by applying a first trained semantic mapping model to the item title. The first trained semantic mapping model includes a first semantic mapping framework. The at least one item embedding is compared to a set of keyword embeddings representative of a set of platform-relevant keywords and a set of item-specific recommended keywords is selected from the set of platform-relevant keywords based on a similarity between the at least one item embedding and each embedding in the set of keyword embeddings. The item title is modified to include at least one of the set of item-specific recommended keywords and an interface including the modified item title is generated.

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 an item data structure including an item title;
generate at least one item embedding by applying a first trained semantic mapping model to the item title, wherein the first trained semantic mapping model includes a first semantic mapping framework;
compare the at least one item embedding to a set of keyword embeddings representative of a set of platform-relevant keywords;
select a set of item-specific recommended keywords from the set of platform-relevant keywords based on a similarity between the at least one item embedding and each embedding in the set of keyword embeddings;
modify the item title to include at least one item-specific recommended keyword in the set of item-specific recommended keywords;
update the item data structure to include the modified item title;
receive a search query; and
generate an interface responsive to the search query, wherein the interface includes the item data structure and the modified item title.

2 . The system of claim 1 , wherein the set of keyword embeddings are generated by a second trained semantic mapping model, and wherein the second trained semantic mapping model includes the first semantic mapping framework.

3 . The system of claim 1 , wherein the at least one item embedding is compared to the set of keyword embeddings by determining a pairwise distance between the at least one item embedding and each embedding in the set of keyword embeddings in vector space.

4 . The system of claim 3 , wherein the set of item-specific recommended keywords includes keywords associated with keyword embeddings located within a predetermined pairwise distance of the at least one item embedding in vector space.

5 . The system of claim 1 , wherein the set of keyword embeddings are generated prior to receiving the item data structure.

6 . The system of claim 1 , wherein the processor is configured to read the set of instructions to filter the set of recommended augmentation keywords to remove a set of removal words.

7 . The system of claim 1 , wherein the processor is configured to read the set of instructions to deduplicate the set of item-specific recommended keywords based on the item title.

8 . The system of claim 1 , wherein the processor is configured to read the set of instructions to deduplicate the set of item-specific recommended keywords based on a prior item-specific recommended keyword.

9 . The system of claim 8 , wherein the deduplication includes:
generating a first embedding for the prior item-specific recommended keyword using a second semantic mapping model, wherein the second semantic mapping model includes a second semantic mapping framework; generating a second embedding for a next item-specific recommended keyword in the set of item-specific recommended keywords using the second semantic mapping model; determining a similarity between the first embedding and the second embedding; and deduplicating the next item-specific recommended keyword when the similarity is above a predetermined threshold.

10 . A computer-implemented method, comprising:
receiving an item data structure including an item title; generating at least one item embedding by applying a first trained semantic mapping model to the item title, wherein the first trained semantic mapping model includes a first semantic mapping framework; comparing the at least one item embedding to a set of keyword embeddings representative of a set of platform-relevant keywords; selecting a set of item-specific recommended keywords from the set of platform-relevant keywords based on a similarity between the at least one item embedding and each embedding in the set of keyword embeddings; modifying the item title to include at least one of the set of item-specific recommended keywords; updating the item data structure to include the modified item title; receiving a search query; and generating an interface responsive to the search query, wherein the interface includes the item data structure and the modified item title.

11 . The method of claim 10 , wherein the set of keyword embeddings are generated by a second trained semantic mapping model, and wherein the second trained semantic mapping model includes the first semantic mapping framework.

12 . The method of claim 10 , wherein the at least one item embedding is compared to the set of keyword embeddings by determining a pairwise distance between the at least one item embedding and each embedding in the set of keyword embeddings in vector space.

13 . The method of claim 12 , wherein the set of item-specific recommended keywords includes keywords associated with keyword embeddings located within a predetermined pairwise distance of the at least one item embedding in vector space.

14 . The method of claim 10 , wherein the set of keyword embeddings are generated prior to receiving the item data structure.

15 . The method of claim 10 , comprising filtering the set of item-specific recommended keywords to remove a set of removal words.

16 . The method of claim 10 , comprising deduplicating the set of item-specific recommended keywords based on the item title.

17 . The method of claim 10 , comprising deduplicating the set of item-specific recommended keywords based on a prior item-specific recommended keyword.

18 . The method of claim 17 , wherein the deduplication includes:
generating a first embedding for the prior item-specific recommended keyword using a second semantic mapping model, wherein the second semantic mapping model includes a second semantic mapping framework; generating a second embedding for a next item-specific recommended keyword in the set of recommended augmentation keywords using the second semantic mapping model; determining a similarity between the first embedding and the second embedding; and deduplicating the next item-specific recommended keyword when the similarity is above a predetermined threshold.

19 . A non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:
receiving an item data structure including an item title; generating at least one item embedding by applying a first trained semantic mapping model to the item title, wherein the first trained semantic mapping model includes a first semantic mapping framework; generating a set of keyword embeddings for a set of platform-relevant keywords by applying a second trained semantic mapping model to each keyword in the set of platform-relevant keywords, wherein the second trained semantic mapping model includes the first semantic mapping framework; determining a pairwise distance between the at least one item embedding and each embedding in the set of keyword embeddings in vector space; selecting a set of item-specific recommended keywords from the set of platform-relevant keywords based on a similarity between the at least one item embedding and each embedding in the set of keyword embeddings, wherein the set of item-specific recommended keywords includes keywords associated with keyword embeddings located within a predetermined pairwise distance of the at least one item embedding in vector space; modifying the item title to include at least one item-specific recommended keyword in the set of item-specific

Assignees

• Walmart Apollo Llc

Inventors

• Wang Zigeng
• Kim Jae Young
• Cai Yaotong
• Zhao Xia
• Shen Wei

Classifications

• G06F16/3334 Primary

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title