Method for predicting matching degree between resume and post, and related device

Patent metadata

Field	Value
Publication number	US-12001518-B2
Application number	US-202218083211-A
Country	US
Kind code	B2
Filing date	Dec 16, 2022
Priority date	Dec 17, 2021
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.

First claim

What is claimed is:

1. A method for predicting matching degree between resume information and post information, implemented via a processor, comprising:
receiving, via input interface, the resume information and the post information from storage;
obtaining, via a processor, a first key and a first value of a respective semi-structured post attribute in the post information and a second key and a second value of a respective semi-structured resume attribute in the resume information, the first key, the first value, the second key and the second value being all expressed in text data; and
predicting, via the processor, the matching degree between the resume information and the post information by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the first key and the first value of the respective post attribute, a first source representation corresponding to the post information, the second key and the second value of the respective resume attribute, and a second source representation corresponding to the resume information;
wherein predicting the matching degree between the resume information and the post information comprises:
for the first key and the first value of the respective post attribute, respectively encoding the first key and the first value into a semantic space through the pre-trained language model so as to obtain a first key embedding and a first value embedding, and fusing the first key embedding and the first value embedding so as to obtain a first fused embedding of the post attribute;
encoding the first source representation into the semantic space through the pre-trained language model so as to obtain a first source embedding;
for the second key and the second value of the respective resume attribute, respectively encoding the second key and the second value into the semantic space through the pre-trained language model so as to obtain a second key embedding and a second value embedding, and fusing the second key embedding and the second value embedding so as to obtain a second fused embedding of the resume attribute;
encoding, via the processor, the second source representation into the semantic space through the pre-trained language model so as to obtain a second source embedding; and
performing, via the processor, internal interaction of a first matrix including the first fusion embedding of the respective post attribute so as to obtain a first internal-interaction-attribute embedding matrix and performing internal interaction of a second matrix including the second fusion embedding of the respective resume attribute so as to obtain a second internal-interaction-attribute embedding matrix, with the Transformer encoder;
wherein the fusing and embedding operations are performed on all attributes in and to obtain matrix representations of and :
X J =[j 1 a ;j 2 a ; . . . ;j m a ],
X R =[r 1 a ;r 2 a ; . . . ;r n a ],

where J is the post information, is the resume information, j i a , r j a ∈ d in are fused and embedded expressions of an i-th attribute in and a j-th attribute in , respectively, i∈{1,2, . . . , m}, j∈{1,2, . . . , n}, X j ∈ m×d in is a first matrix and the matrix representation of , X R ∈ n×d in is a second matrix and the matrix representation of , m and n are corresponding numbers of attributes in and , and d in is a vector dimension after an attribute value and an attribute key are fused;
wherein a multi-head self-attention matrix representation of , M J , and a multi-head self-attention matrix representation of , M R , are calculated according to:

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Assignees

• National Univ Of Defense Technology

Inventors

• Chen Honghui
• Shao taihua
• Song Chengyu
• Jiang Miao
• Wang Mengru
• Zhang Xin
• Cai Fei
• Liu Dengfeng
• Wang Siyuan

Classifications

• G06F18/241 Primary

  relating to the classification model, e.g. parametric or non-parametric approaches · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06Q10/1053 Primary

  Employment or hiring · CPC title

• G06Q10/105 Primary

  Human resources · CPC title