Methods and apparatus for electronically determining items to advertise

Patent metadata

Field	Value
Publication number	US-2021201351-A1
Application number	US-201916730514-A
Country	US
Kind code	A1
Filing date	Dec 30, 2019
Priority date	Dec 30, 2019
Publication date	Jul 1, 2021
Grant date	—

Abstract

This application relates to apparatus and methods for automatically determining items to advertise, such as on a website, based on user search queries. In some examples, a computing device trains a machine learning process with user session data that identifies user search queries and related search context. The computing device may execute the trained machine learning process to determine one or more predicted items for each of a plurality of search queries. The predicted items may be stored in a database. The computing device may receive a request for recommended items to advertise in connection with a current user query. In response, the computing device determines items for the current user query based on the predicted items stored in the database. In some examples, the computing device determines additional items based on search queries similar to the current search query. Advertisements for the items may then be displayed.

First claim

What is claimed is:

1 . A system comprising:
a computing device configured to:
obtain session data identifying a plurality of search queries and corresponding context for a plurality of users;
determine a corresponding anchor item for each of the plurality of search queries based on the context corresponding to each of the plurality of search queries;
determine at least one recommended item for each of the plurality of search queries based on each of the plurality of search queries and the corresponding anchor item; and
store the at least one recommended item for each of the plurality of search queries and the corresponding anchor items in an item recommendation database.

2 . The system of claim 1 , wherein determining the at least one recommended item for each of the plurality of search queries comprises executing a trained machine learning model to generate the at least one recommended item based on each of the plurality of search queries and the corresponding anchor item.

3 . The system of claim 2 , wherein the trained machine learning model is trained with training data identifying search query and anchor item pairs.

4 . The system of claim 1 , wherein the computing device is configured to:
generate an embedding for each of the at least one recommended items; determine at least one additional recommended item for each of the at least one recommended items based on the embeddings; and store the at least one additional recommended item for each of the at least one recommended items in the item recommendation database.

5 . The system of claim 4 , wherein generating the embedding for each of the at least one recommended items comprises executing an artificial neural network.

6 . The system of claim 1 , wherein the computing device is configured to determine a plurality of most frequent search queries of the plurality of search queries, wherein storing the at least one recommended item for each of the plurality of search queries and the corresponding anchor items in the item recommendation database comprises:
determining, for each of the plurality of search queries that is not in the plurality of most frequent search queries, a closest query of the plurality of most frequent search queries; and storing a pointer to the closest query of the plurality of most frequent search queries instead of the at least one recommended item.

7 . The system of claim 1 , wherein the computing device is configured to:
receive current session data identifying a current search query; and determine a first recommended item from the at least one recommended items stored in the item recommendation database based on the current search query.

8 . The system of claim 7 , wherein the current search query comprises a plurality of words, and wherein the computing device is configured to normalize the current search query by:
removing punctuation from the plurality of words; removing stop words from the plurality of words; and stemming the plurality of words, wherein determining the first recommended item from the at least one recommended items stored in the item recommendation database is based on the normalized current search query.

9 . The system of claim 1 , wherein storing the at least one recommended item for each of the plurality of search queries and the corresponding anchor items in the item recommendation database comprises:
generating a first plurality of word embeddings corresponding to each of the plurality of search queries; generating a first query embedding based on the first plurality of word embeddings corresponding to each of the plurality of search queries; and storing the first query embedding for each of the plurality of search queries in the item recommendation database.

10 . The system of claim 9 , wherein the computing device is configured to:
receive current session data identifying a current search query; generate a second plurality of word embeddings based on the current search query; generate a second query embedding based on the second plurality of word embeddings; and determine a first recommended item from the at least one recommended items stored in the item recommendation database based on comparing the second query embedding to at least a portion of the first query embeddings for the plurality of search queries stored in the item recommendation database.

11 . The system of claim 10 , wherein the computing device is configured to:
determine a user identification and a webpage context based on the current session data; determine a historic search query based on the user identification; determine a relevance of the historic search query to the webpage context; and determine a second recommended item from the at least one recommended items stored in the item recommendation database when the relevance is beyond a threshold.

12 . A method comprising:
obtaining session data identifying a plurality of search queries and corresponding context for a plurality of users; determining a corresponding anchor item for each of the plurality of search queries based on the context corresponding to each of the plurality of search queries; determining at least one recommended item for each of the plurality of search queries based on each of the plurality of search queries and the corresponding anchor item; and storing the at least one recommended item for each of the plurality of search queries and the corresponding anchor items in an item recommendation database.

13 . The method of claim 12 wherein determining the at least one recommended item for each of the plurality of search queries comprises executing a trained machine learning model to generate the at least one recommended item based on each of the plurality of search queries and the corresponding anchor item.

14 . The method of claim 13 wherein the trained machine learning model is trained with training data identifying search query and anchor item pairs.

15 . The method of claim 12 comprising:
generating an embedding for each of the at least one recommended items;
determining at least one additional recommended item for each of the at least one recommended items based on the embeddings; and
storing the at least one additional recommended item for each of the at least one recommended items in the item recommendation database.

16 . The method of claim 12 comprising:
receiving current session data identifying a current search query; and
determining a first recommended item from the at least one recommended items stored in the item recommendation database based on the current search query.

17 . The method of claim 12 wherein storing the at least one recommended item for each of the plurality of search queries and the corresponding anchor items in the item recommendation database comprises:
generating a first plurality of word embeddings corresponding to each of the plurality of search queries;
generating a first query embedding based on the first plurality of word embeddings corresponding to each of the plurality of search queries; and
storing the first query embedding for each of the plurality of search queries in the item recommendation database.

18 . 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:
obtaining session data identifying a plurality of search queries and corresponding context for a plurality of users; determining a corresponding anchor item for each of the plura

Assignees

• Walmart Apollo Llc

Inventors

• Nag Kaushiki
• Achan Kannan
• Manoharan Shirpaa
• Wadhwa Soumya
• Yalla Surya
• Duraisamy Swaminathan

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q30/0256 Primary

  User search · CPC title