Cross-regional talent flow intention analysis method, electronic device, and storage medium

Patent metadata

Field	Value
Publication number	US-2023230038-A1
Application number	US-202217945168-A
Country	US
Kind code	A1
Filing date	Sep 15, 2022
Priority date	Jan 19, 2022
Publication date	Jul 20, 2023
Grant date	—

Abstract

There is provided a method for cross-regional talent flow intention analysis, an electronic device, and a storage medium, which relates to technical fields such as big data processing and data statistics and analysis. A specific implementation solution involves: constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.

First claim

What is claimed is:

1 . A method for cross-regional talent flow intention analysis, comprising:
constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.

2 . The method of claim 1 , wherein the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result comprises:
analyzing regional talent flow indicators of regions based on the talent flow intention network; and/or mining a regional flow cluster based on the talent flow intention network; and analyzing interregional flow indicators in the regional flow cluster.

3 . The method of claim 1 , wherein the constructing a talent flow intention network based on search data in a network comprises:
mining cross-regional job search behavior information based on the search data in the network within the preset period of time; and establishing the talent flow intention network based on the cross-regional job search behavior information.

4 . The method of claim 3 , wherein the establishing the talent flow intention network based on the cross-regional job search behavior information comprises:
constructing the talent flow intention network by taking regions in the cross-regional job search behavior information as nodes in the network, connection lines between nodes corresponding to two regions crossed by the cross-regional job search behavior information as edges, and a number of frequencies of the cross-regional job search behavior information within the preset time period as intensity of the corresponding edges.

5 . The method of claim 4 , wherein the edges of the talent flow intention network are directed edges.

6 . The method of claim 1 , subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, further comprising:
displaying the talent flow intention analysis result.

7 . The method of claim 1 , wherein subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, the method further comprises:
training a talent flow intention analysis model based on the talent flow intention network and the talent flow intention analysis result.

8 . The method of claim 7 , further comprising:
acquiring a target talent flow intention network; and predicting a target talent flow intention analysis model based on the target talent flow intention network by using the talent flow intention analysis model.

9 . The method of claim 2 , wherein subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, the method further comprises:
training a talent flow intention analysis model based on the talent flow intention network and the talent flow intention analysis result.

10 . The method of claim 3 , wherein subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, the method further comprises:
training a talent flow intention analysis model based on the talent flow intention network and the talent flow intention analysis result.

11 . An electronic device, comprising:
at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method for cross-regional talent flow intention analysis, wherein the method comprises:
constructing a talent flow intention network based on search data in a network within a preset period of time; and
performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result.

12 . The electronic device of claim 11 , wherein the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result comprises:
analyzing regional talent flow indicators of regions based on the talent flow intention network; and/or mining a regional flow cluster based on the talent flow intention network; and analyze interregional flow indicators in the regional flow cluster.

13 . The electronic device of claim 11 , wherein the constructing a talent flow intention network based on search data in a network comprises:
mining cross-regional job search behavior information based on the search data in the network within the preset period of time; and establishing the talent flow intention network based on the cross-regional job search behavior information.

14 . The electronic device of claim 13 , wherein the establishing the talent flow intention network based on the cross-regional job search behavior information comprises:
constructing the talent flow intention network by taking regions in the cross-regional job search behavior information as nodes in the network, connection lines between nodes corresponding to two regions crossed by the cross-regional job search behavior information as edges, and a number of frequencies of the cross-regional job search behavior information within the preset time period as intensity of the corresponding edges.

15 . The electronic device of claim 14 , wherein the edges of the talent flow intention network are directed edges.

16 . The electronic device of claim 11 , subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, further comprising:
displaying the talent flow intention analysis result.

17 . The electronic device of claim 11 , subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, the method further comprises:
training a talent flow intention analysis model based on the talent flow intention network and the talent flow intention analysis result.

18 . The electronic device of claim 17 , further comprising:
acquiring a target talent flow intention network; and predicting a target talent flow intention analysis model based on the target talent flow intention network by using the talent flow intention analysis model.

19 . The electronic device of claim 12 , subsequent to the performing cross-regional talent flow intention analysis based on the talent flow intention network to obtain a talent flow intention analysis result, the method further comprises:
training a talent flow intention analysis model based on the talent flow intention network and the talent flow intention analysis result.

20 . A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a method for cross-regional talent flow intention analysis, wherein the method comprises:
constructing a talent flow intention network based on search data in a network within a preset period of time; and performing cross-regional

Assignees

• Beijing Baidu Netcom Sci & Tech Co Ltd

Inventors

• Sun Ying
• Zhu Hengshu
• Qin Chuan
• Wang Peng
• Xiong Hui

Classifications

• G06Q10/1053 Primary

  Employment or hiring · CPC title

• G06F16/906

  Clustering; Classification · CPC title

• G06Q30/0203 Primary

  Market surveys; Market polls · CPC title

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title

• G06N3/08

  Learning methods · CPC title