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

Patent metadata

Field	Value
Publication number	US-2023041339-A1
Application number	US-202117403067-A
Country	US
Kind code	A1
Filing date	Aug 16, 2021
Priority date	Jul 23, 2021
Publication date	Feb 9, 2023
Grant date	—

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; sending the behavior prediction information of the target user to a server; 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 first user behavior embedding engine is implemented by a neural network, 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 6 , further comprising:
updating the trained first user behavior embedding engine by quantizing values of the multiple weights of the multiple branches.

9 . A method implemented at a server, comprising:
receiving behavior prediction information of a target user from a client; generating, by a second item embedding engine in the server, recommendation information about a group of items based on feature information of the group of items; determining, based on the behavior prediction information of the target user and the recommendation information about the group of items, a target item recommended for the target user from the group of items; and sending information about the target item to the client.

10 . The method according to claim 9 , wherein the server comprises a second user behavior embedding engine, the second user behavior embedding engine and the second item embedding engine are implemented by a deep semantic similarity model (DSSM), and the method further comprises:
receiving a first group of parameters of the DSSM from the client; and updating the DSSM by using the first group of parameters.

11 . The method according to claim 10 , further comprising:
sending an updated second group of parameters of the DSSM to the client.

12 . 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; sending the behavior prediction information of the target user to a server; and receiving information about a target item recommended for the target user from the server.

13 . The device according to claim 12 , 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.

14 . The device according to claim 13 , 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.

15 . The device according to claim 13 , 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.

16 . A device implemented at a server, 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 execute actions comprising: receiving behavior prediction information of a target user from a client; generating recommendation information about a group of items based on feature information of the group of items through a second item embedding engine in the server; determining, based on the behavior prediction information of the target user and the recommendation information about the group of items, a target item recommended for the target user from the group of items; and sending information about the target item to the client.

17 . The device according to claim 16 , wherein the server comprises a second user behavior embedding engine, the second user behavior embedding engine and the second item embedding engine are implemented by a deep semantic similarity model (DSSM), and the actions further comprise:
receiving a first group of parameters of the DSSM from the client; and updating the DSSM by using the first group of parameters.

18 . The device according to claim

Assignees

• Emc Ip Holding Co Llc

Inventors

• Ni Jiacheng
• Liu Jinpeng
• Sha Danqing

Classifications

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/082

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

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title