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

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 :
• XJ=[j1a;j2a;
• . . . ;jma],
• XR=[r1a;r2a;
• . . . ;rna],
• where J is the post information, is the resume information, jia, rja∈din 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}, Xj∈m×din is a first matrix and the matrix representation of , XR∈n×din is a second matrix and the matrix representation of , m and n are corresponding numbers of attributes in and , and din is a vector dimension after an attribute value and an attribute key are fused;
• wherein a multi-head self-attention matrix representation of , MJ, and a multi-head self-attention matrix representation of , MR, are calculated according to:
• M
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• where QJh, QRh∈din×dq, KJh, KRh∈din×dk, VJh, VRh∈din×dvk and OJ, OR∈H·dv×din are trainable network parameters;
• h∈{1,2, . . . , H}, H represents a number of headers of the Transformer encoder, dq=dk=dv=din/H, MJh and MRh are self-attention matrix representations of and at an h-th head;
• MJ and MR are input into a feedforward layer of the Transformer encoder to obtain internal interaction representations of and as follows:
• M
• J
• =
• feedforward
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• where jia′, rja′∈din are internal-interaction-attribute embeddings of the i-th attribute in and the j-th attribute in , i∈{1,2, . . . , m} and j∈{1,2, . . . , n}, respectively;
• and
• wherein the prediction model is trained by minimizing a binary cross entropy loss according to:
• =−1/NΣi=1N(i log(′i)+(1−i)log(1−′i)),
• where i is a true matching degree of an i-th training instance;
• ′i is a predicted matching degree of the i-th training instance generated by the model;
• N is a total number of training instances,
• thus predicting an accurate matching degree between the resume information and the post information to obtain rich representations of the resume information and the post information;
• and
• presenting, via output interface, rich representations of the resume information and the post information, which comprising the resume information, the post information and the accurate matching degree, wherein when the accurate matching degree is 1 indicates match, and when the accurate matching degree is 0 indicates no match.