ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

10/05/2018
by   Yong Luo, et al.
0

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.

READ FULL TEXT
research
06/23/2020

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Fetal brain MRI is useful for diagnosing brain abnormalities but is chal...
research
10/01/2020

SESQA: semi-supervised learning for speech quality assessment

Automatic speech quality assessment is an important, transversal task wh...
research
06/05/2019

Towards Document Image Quality Assessment: A Text Line Based Framework and A Synthetic Text Line Image Dataset

Since the low quality of document images will greatly undermine the chan...
research
09/14/2020

Data Quality Evaluation using Probability Models

This paper discusses an approach with machine-learning probability model...
research
03/20/2018

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

This work tackles the automatic fine-grained slide quality assessment pr...
research
08/08/2011

A new embedding quality assessment method for manifold learning

Manifold learning is a hot research topic in the field of computer scien...
research
01/12/2016

Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking

Re-speaking is a mechanism for obtaining high quality subtitles for use ...

Please sign up or login with your details

Forgot password? Click here to reset