Automatically determining by a federated search ads to be presented on a user interface

Patent metadata

Field	Value
Publication number	US-2023245171-A1
Application number	US-202217589873-A
Country	US
Kind code	A1
Filing date	Jan 31, 2022
Priority date	Jan 31, 2022
Publication date	Aug 3, 2023
Grant date	—

Abstract

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, an ad request. The method also can include retrieving ad candidates from an ad database. The method further can include determining a respective ad ranking score for each of the ad candidates, based at least in part on the ad request and respective historical retrieval scores for each of the ad candidates. In some embodiments, each of the respective historical retrieval scores for each of the ad candidates is determined at least in part by a multi-channel search engine and a respective historical search query, by: (a) determining, by a semantic search model of the multi-channel search engine, one or more semantic search results from the ad candidates and a respective semantic ranking score for each of the one or more semantic search results, based on a respective query vector embedding of the respective historical search query and a respective ad vector embedding of each of the one or more semantic search results; (b) determining, by a syntactic search model of the multi-channel search engine, one or more syntactic search results from the ad candidates and a respective syntactic ranking score for each of the one or more syntactic search results, based on the respective historical search query; (c) unifying the respective semantic ranking score for each of the one or more semantic search results; (d) unifying the respective syntactic ranking score for each of the one or more syntactic search results; and (e) merging the one or more semantic search results and the one or more syntactic search results into one or more historical ad candidates based on the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified. The one or more historical ad candidates, as merged, can comprise the ad candidates, and each of the respective historical retrieval scores for each of the ad candidates can be the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified. The method additionally can include determining one or more ad finalists based at least in part on the respective ad ranking score for each of the ad candidates. Moreover, the method can include transmitting, via the computer network, the one or more ad finalists to be displayed on a user interface. Other embodiments are described.

First claim

1 . A system comprising:
one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform:
receiving, from a user computer and via a computer network, an ad request;
retrieving ad candidates from an ad database;
using a multi-channel search engine to determine a respective ad ranking score for each of the ad candidates, comprising:
using a semantic search model of the multi-channel search engine to determine one or more semantic search results from the ad candidates and a respective semantic ranking score for each of the one or more semantic search results, based on a respective query vector embedding of a respective historical search query for each of the ad candidates and a respective ad vector embedding of each of the one or more semantic search results;
using a syntactic search model of the multi-channel search engine to determine one or more syntactic search results from the ad candidates and a respective syntactic ranking score for each of the one or more syntactic search results, based on the respective historical search query for each of the ad candidates;
making the one or more semantic search results and the one or more syntactic search results comparable with each other by:
unifying the respective semantic ranking score for each of the one or more semantic search results; and
unifying the respective syntactic ranking score for each of the one or more syntactic search results; and

after making the one or more semantic search results and the one or more syntactic search results comparable with each other, merging the one or more semantic search results and the one or more syntactic search results into one or more historical ad candidates based on the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified, wherein:
the respective ad ranking score for each of the ad candidates is determined based at least in part on the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified;

determining one or more ad finalists from the ad candidates based at least in part on the respective ad ranking score for each of the ad candidates; and
transmitting, via the computer network, the one or more ad finalists to be displayed on a user interface of the user computer.

2 . The system in claim 1 , wherein:
the respective historical search query is received, via the computer network, within a first predetermined time period prior to a current time.

3 . The system in claim 1 , wherein:
unifying the respective semantic ranking score for each of the one or more semantic search results further comprises normalizing the respective semantic ranking score into a predetermined range; and unifying the respective syntactic ranking score for each of the one or more syntactic search results further comprises normalizing the respective syntactic ranking score into the predetermined range.

4 . The system in claim 1 , wherein:
merging the one or more semantic search results and the one or more syntactic search results into the one or more historical ad candidates further comprises one or more of:
removing one or more low-semantic-score semantic search items from the one or more semantic search results, based on one or more of: a predetermined unified semantic score threshold, or a predetermined semantic search result count;
removing one or more low-syntactic-score syntactic search items from the one or more syntactic search results, based on one or more of: a predetermined unified syntactic score threshold, or a predetermined syntactic search result count;
removing one or more low-ad-ranking-score ad items from the one or more historical ad candidates, as merged, based on one or more of: a predetermined unified ad score threshold, or a predetermined ad candidate count; or discarding duplicate ad items from the one or more historical ad candidates.

5 . The system in claim 1 , wherein:
using the multi-channel search engine to determine the respective ad ranking score for each of the ad candidates further comprises determining the respective ad ranking score for each of the ad candidates based at least in part on: (a) the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified, and (b) a respective predicted click-through-rate for each of the ad candidates.

6 . The system in claim 5 , wherein:
the respective predicted click-through-rate for each of the ad candidates is determined based on respective historical impressions and respective historical clicks for each of the ad candidates occurring within a second predetermined time period prior to a current time.

7 . The system in claim 1 , wherein:
using the multi-channel search engine to determine the respective ad ranking score for each of the ad candidates further comprises determining the respective ad ranking score for each of the ad candidates based at least in part on: (a) the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified, and (b) a respective average cost-per-click for each of the ad candidates.

8 . The system in claim 7 , wherein:
the respective average cost-per-click for each of the ad candidates is determined based on respective historical auction costs and respective historical clicks for each of the ad candidates occurring within a third predetermined time period prior to a current time.

9 . The system in claim 1 , wherein:
using the multi-channel search engine to determine the respective ad ranking score for each of the ad candidates further comprises determining the respective ad ranking score by:
(α category *norm(RS)+(1−α category )*pCTR)*CPC,

wherein:
α category is a respective weight determined based on a respective category, category, associated with each of the ad candidates;
norm(RS) is the respective semantic ranking score, as unified, or the respective syntactic ranking score, as unified, for each of the ad candidates;
pCTR is a respective predicted click-through-rate for each of the ad candidates; and
CPC is a respective average cost-per-click for each of the ad candidates.

10 . The system in claim 9 , wherein:
determining the respective ad ranking score for each of the ad candidates further comprises determining the respective weight, α category , for the respective category associated with each of the ad candidates, based on a respective average predicted click-through-rate for each of the ad candidates, a respective average retrieval score for each of the ad candidates, a respective average expected click-through-rate for each of the ad candidates, and a respective average cost-per-click for each of the ad candidates.

11 . A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
receiving, from a user computer and via a computer network, an ad request; retrieving ad candidates from an ad database; using a multi-channel search engine to determine a respective ad ranking score for each of the ad candidates, comprising:
using a semantic search model of the multi-channel search engine to determine one or more semantic search results from the ad candidates and a respective semantic ranking score for each of the one or more semantic search results, based on a respective query vector embedding of a respective historical search query for each of the ad candidates and a respective ad vector embedding of each of the one or more

Assignees

• Walmart Apollo Llc

Inventors

• Niu Yichuan
• FANG Biyi
• Xue Yangbing
• Upreti Kritika
• Singh Ashutosh
• Kumar Vivek
• Yan Haibo
• Pelyushenko Valeriy V
• Garigipati Rajesh
• Korlimarla Jayanth
• Xu Dong
• Wen Musen
• Guo Stephen Dean

Classifications

• G06Q30/0257 Primary

  User requested · CPC title

• G06Q30/0246

  Traffic · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06Q30/0256

  User search · CPC title