Method, device, and computer program product for user behavior prediction

Patent metadata

Field	Value
Publication number	US-12229815-B2
Application number	US-202117403067-A
Country	US
Kind code	B2
Filing date	Aug 16, 2021
Priority date	Jul 23, 2021
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

Embodiments of the present disclosure relate to a method, a device, and a computer program product for user behavior prediction. In some embodiments, at a client, a first user behavior embedding engine in the client generates behavior prediction information of a target user based on feature information of the target user. The client sends the behavior prediction information of the target user to a server, and receives information about a target item recommended for the target user from the server. Such method enables user privacy-related information to be processed only locally, thereby not only ensuring user privacy and security, but also significantly reducing overall resource overhead.

First claim

What is claimed is:

1. A method implemented at a client, comprising:
generating, by a first user behavior embedding engine in the client, behavior prediction information of a target user based on feature information of the target user, wherein the first user behavior embedding engine is implemented by a neural network, and wherein the neural network is compressed at the client;
mapping the feature information of the target user to the behavior prediction information of the target user;
sending the behavior prediction information of the target user to a server, the server comprising at least a second neural network; and
receiving information about a target item recommended for the target user from the server.

2. The method according to claim 1 , further comprising:
jointly training the first user behavior embedding engine and a first item embedding engine in the client by using a training data set, wherein the first item embedding engine is used to generate item recommendation information based on feature information of an item.

3. The method according to claim 2 , wherein jointly training the first user behavior embedding engine and the first item embedding engine comprises:
generating, by the first user behavior embedding engine, behavior prediction information of a reference user based on feature information of the reference user in the training data set;
generating, by the first item embedding engine, recommendation information of a reference item based on feature information of the reference item in the training data set;
determining a degree of matching between the reference user and the reference item based on the behavior prediction information of the reference user and the recommendation information of the reference item; and
updating the first user behavior embedding engine and the first item embedding engine based on comparison of the determined degree of matching with a reference degree of matching between the reference user and the target item in the training data set.

4. The method according to claim 2 , wherein the first user behavior embedding engine and the first item embedding engine are implemented by a deep semantic similarity model (DSSM), and the method further comprises:
sending a first group of parameters of the DSSM to the server after the training.

5. The method according to claim 4 , further comprising:
receiving a second group of parameters of the DSSM from the server; and
updating the DSSM based on the second group of parameters.

6. The method according to claim 2 , wherein the neural network comprises multiple branches, and each branch of the multiple branches has a weight associated with an influence of the branch on user behavior prediction.

7. The method according to claim 6 , further comprising:
updating the trained first user behavior embedding engine by deleting, based on multiple weights of the multiple branches, branches whose influences on the user behavior prediction are lower than a threshold influence from the multiple branches.

8. The method according to claim 7 , further comprising:
updating the trained first user behavior embedding engine by quantizing values of the multiple weights of the multiple branches.

9. A device implemented at a client, comprising:
a processor; and
a memory having computer-executable instructions stored therein, wherein the computer-executable instructions, when executed by the processor, cause the device to perform actions comprising:
generating, by a first user behavior embedding engine in the client, behavior prediction information of a target user based on feature information of the target user, wherein the first user behavior embedding engine is implemented by a neural network, and wherein the neural network is compressed at the client;
mapping the feature information of the target user to the behavior prediction information of the target user;
sending the behavior prediction information of the target user to a server, the server comprising at least a second neural network; and
receiving information about a target item recommended for the target user from the server.

10. The device according to claim 9 , wherein the actions further comprise:
jointly training the first user behavior embedding engine and a first item embedding engine in the client by using a training data set, wherein the first item embedding engine is used to generate item recommendation information based on feature information of an item.

11. The device according to claim 10 , wherein jointly training the first user behavior embedding engine and the first item embedding engine comprises:
generating, by the first user behavior embedding engine, behavior prediction information of a reference user based on feature information of the reference user in the training data set;
generating, by the first item embedding engine, recommendation information of a reference item based on feature information of the reference item in the training data set;
determining a matching degree of the reference user and the reference item based on the behavior prediction information of the reference user and the recommendation information of the reference item; and
updating the first user behavior embedding engine and the first item embedding engine based on comparison of the determined matching degree with a reference matching degree of the reference user and the target item in the training data set.

12. The device according to claim 10 , wherein the first user behavior embedding engine and the first item embedding engine are implemented by a deep semantic similarity model (DSSM), and the actions further comprise:
sending a first group of parameters of the DSSM to the server after the training.

13. A computer program product tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein the machine-executable instructions, when executed, cause a machine to perform the method according to claim 1 .

Assignees

• Emc Ip Holding Co Llc

Inventors

• Ni Jiacheng
• Liu Jinpeng
• Sha Danqing

Classifications

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title