Resume-Post Matching Using Cascaded Language Models
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Solution Overview
Problem
Existing person-post matching methods fail to accurately predict matching degrees due to the neglect of semi-structured multivariate attributes, leading to inaccurate results, as they only consider free-text attributes and ignore the significance of attributes like education history and salary.
Innovation Solution
A method using a cascaded pre-trained language model, Transformer encoder, and single label classification model to predict matching degrees by encoding and fusing semi-structured key and value pairs from both post and resume information, performing internal and external interactions to capture attribute relationships, and generating accurate matching scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only free-text attributes are considered in person-post matching, then the matching process is simple, but the prediction accuracy deteriorates due to ignoring semi-structured multivariate attributes
Solution Approach 1:
The patent segments the matching process into two distinct parts: (1) processing free-text attributes using traditional text matching methods, and (2) processing semi-structured multivariate attributes (education history, salary, etc.) using a dedicated prediction model. This segmentation allows each type of attribute to be handled with appropriate methods, improving overall accuracy without unnecessarily complicating the entire system.
Solution Approach 2:
The prediction model is designed to handle multiple types of semi-structured attributes universally (education history, salary, work experience, etc.) through a unified architecture that processes key-value pairs. This multi-functional approach enables the system to accommodate various attribute types without requiring separate processing pipelines for each, balancing complexity and comprehensiveness.
2Measurement precision
If semi-structured multivariate attributes are included in the matching process, then the prediction accuracy improves, but the device complexity increases due to the need for sophisticated prediction models
Solution Approach 1:
The system segments attribute processing by type: free-text attributes are handled with simple text matching, while semi-structured attributes are processed by the prediction model. This segmentation prevents the entire system from becoming unnecessarily complex by applying sophisticated methods only where needed.
Solution Approach 2:
The patent transforms semi-structured attributes into a standardized key-value pair representation with unified data structures. By changing the parameter representation format and using embedding techniques to convert attributes into numerical vectors, the system simplifies the input processing for the prediction model, reducing complexity while maintaining accuracy.
Data Source
AI summary
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.