Target user directing method and apparatus and computer storage medium

Patent metadata

Field	Value
Publication number	US-11004107-B2
Application number	US-201816005936-A
Country	US
Kind code	B2
Filing date	Jun 12, 2018
Priority date	May 5, 2016
Publication date	May 11, 2021
Grant date	May 11, 2021

Abstract

A target user directing method and apparatus and provided. The method includes determining a similarity between each of candidate users and a seed user by using a similarity model. A conversion prediction model is used to predict a probability that each of the candidate users performs a conversion operation on to-be-delivered information. One or more target users for the to-be-delivered information are selected from the candidate users according to the similarity that is determined and the probability that is predicted for each of the candidate users. The to-be-delivered information is transmitted to the one or more target users.

First claim

What is claimed is:

1. A method comprising:
extracting, by at least one processor as a positive example feature for training a similarity model, a user feature of a seed user as a first positive example user;
extracting, by the at least one processor as a negative example feature for training the similarity model, a user feature of a first negative example user; and
training, by the at least one processor, the similarity model using the first positive example feature and the first negative example feature;
determining, by the at least one processor, a similarity between each of a plurality of candidate users and a seed user using the trained similarity model;
pushing, by the at least one processor, a digital advertisement to a social application interface of candidate users whose similarity with the seed user is greater than a threshold similarity;
extracting, by the at least one processor as a second positive example user, according to data of the pushed digital advertisement, a user performing a click operation, an attention operation, or a purchase operation on the pushed digital advertisement through the social application interface;
extracting, by the at least one processor as a second negative example user, according to data of the pushed digital advertisement, a user not performing the click operation, the attention operation and the purchase operation on the pushed digital advertisement through the social application interface; and
training, by the at least one processor, a conversion prediction model using an information feature of the pushed digital advertisement, a first user feature of the second positive example user and a second user feature of the second negative example user;
predicting, by the at least one processor using the trained conversion prediction model, a probability that each of the plurality of candidate users will perform a click operation, an attention operation, or a purchase operation on a to-be-delivered digital advertisement;
determining a first weight of the similarity and a second weight corresponding to the probability for each of the plurality of candidate users; and
calculating a plurality of directing scores based on the similarity, the first weight, the probability, the second weight, and the function relationship between the similarity and the probability; and
selecting, as one or more target users by the at least one processor, candidate users whose directing score satisfies a directing condition, from among the plurality of candidate users; and
pushing the to-be-delivered digital advertisement to a social application interface of the one or more target users.

2. The method according to claim 1 , wherein
the method further comprises:
outputting, using the similarity model, a core feature for determining the similarity, wherein the core feature is a same feature or a similar feature among a plurality of seed users,
wherein the one or more target users are further selected based on the core feature.

3. The method according to claim 1 , wherein the predicting comprises:
extracting an information feature of the to-be-delivered digital advertisement;
extracting a plurality of user features of the plurality of candidate users; and
inputting the information feature and the plurality of user features to the conversion prediction model, to predict the probability.

4. An apparatus comprising:
at least one memory configured to store computer program code; and
at least one processor configured to access the at least one memory and operate according to the computer program code, the computer program code including:
first training code configured to cause at least one of the at least one processor to:
extract, as a positive example feature for training a similarity model, a user feature of a seed user as a first positive example user;
extract, as a negative example feature for training the similarity model, a user feature of a first negative example user; and
train the similarity model using the positive example feature and the negative example feature;

determining code configured to cause at least one of the at least one processor to determine a similarity between each of a plurality of candidate users and a seed user by using the trained similarity model;
pushing code configured to cause at least one of the at least one processor to push a digital advertisement to a social application interface of candidate users whose similarity with the seed user is greater than a threshold similarity;
second training code configured to cause at least one of the at least one processor to:
extract, as a second positive example user, according to data of the pushed digital advertisement, a user performing a click operation, an attention operation or a purchase operation on the pushed digital advertisement through the social application interface;
extract, as a second negative example user, according to data of the pushed digital advertisement, a user not performing the click operation, the attention operation or the purchase operation on the pushed digital advertisement through the social application interface; and
train a conversion prediction model using an information feature of the pushed digital advertisement, a first user feature of the second positive example user and a second user feature of the second negative example user;
prediction code configured to cause at least one of the at least one processor to predict, by using the trained conversion prediction model, a probability that each of the plurality of candidate users performs a click operation, an attention operation or a purchase operation on a to-be-delivered digital advertisement;

selection code configured to cause at least one of the at least one processor to:
determine a first weight of the similarity and a second weight corresponding to the probability for each of the plurality of candidate users;
calculate a plurality of directing scores by using the similarity, the first weight, the probability, the second weight, and a function relationship between the similarity and the probability; and
select, as the one or more target users, candidate users whose directing score satisfies a directing condition, from among the plurality of candidate users; and

transmitting code configured to cause at least one of the at least one processor to push the to-be-delivered digital advertisement to a social application interface of the one or more target users.

5. The apparatus according to claim 4 , wherein the computer program code further comprises output code configured to cause at least one of the at least one processor to output, by using the similarity model, a core feature for determining the similarity, wherein the core feature is a same feature or a similar feature among a plurality of seed users,
wherein the one or more target users are further selected based on the core feature.

6. The apparatus according to claim 4 , wherein the prediction code is further configured to cause at least one of the at least one processor to:
extract an information feature of the to-be digital advertisement;
extract a plurality of user features of the plurality of candidate users; and
input the information feature and the plurality of user features to the conversion prediction model, to predict the probability.

7. A non-transitory computer readable storage medium storing a computer program which, when executed by a computer, performs the following operations:
determining a similarity between each of a plurality of candidate users and a seed user using a similarity model;
predicting, using a conversion prediction model, a probability that each of the plurality of candidate users will perform a click operation, an attention operation, or a purchase operation on a to-b

Assignees

• Tencent Tech Shenzhen Co Ltd

Inventors

• Huang Hao
• Huang dong bo
• Chen Ge

Classifications

• G06Q30/0269

  based on user profile or attribute · CPC title

• G06Q30/0267

  Wireless devices · CPC title

• G06Q30/0243 Primary

  Comparative campaigns · CPC title

• G06Q30/0277

  Online advertisement · CPC title

• G06Q30/0257 Primary

  User requested · CPC title