JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (1): 83-90.doi: 10.6040/j.issn.1671-9352.4.2020.131

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Research on matching resumes and positions based on Arc-LSTM

Fei-fei XU1(),Yun-jie XU2   

  1. 1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
    2. Process and IT Platform Software Development BU, COMAC Shanghai Aviation Industrial(Group), Shanghai 200232, China
  • Received:2020-06-15 Online:2021-01-01 Published:2021-01-05

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Abstract:

An automatic scheme is proposed, which combines the deep neural network Arc-LSTM with Word2Vec model. In this paper, the CBOW model is used to extract resume text features. A new deep neural network, Arc-LSTM is put forward, which is optimized by ArcReLU. The experimental results show that Arc-LSTM can effectively improve the classification accuracy and convergence speed, and achieve the exact match between Chinese resumes and positions.

Key words: deep learning, activation function, job matching, ArcReLU, Arc-LSTM

CLC Number: 

  • TP18

Fig.1

ArcReLU activation function"

Fig.2

Arc-LSTM framework map"

Table 1

Resume category"

职位类别 职位名称
管理及产品岗 数据总监, 分析挖掘, 算法、平台研发
算法工程 推荐系统, 搜索排序, 语音语义, 计算机视觉, 自动驾驶, 深度学习, 卫星导航, 机器人, 通信算法, 其他
数据分析 商业、经营分析, 用户、产品分析, 统计分析, 数据挖掘, 广告, 风控, 量化
数据开发 数仓架构, 大数据ETL, 传统ETL, 大数据开发
平台架构 大数据架构, 平台工具开发, 大数据运维

Table 2

Data set"

数据集 条目数 属性 决策属性
Car Evaluation 1 728 buying, maint, doors, persons, lug-boot, safety class values
Adult 9 502 age, workclass, fnlwgt, education, marital-status, relationship, race sex
Avila 12 647 intercolumnar distance, upper margin, lower margin, exploitation, row number, modular ratio, interlinear spacing, weight, peak number class

Table 3

Dictionary mapping"

实际标题 映射类别
名字 姓名
姓名 姓名
性别 性别
工作地点 城市
工作经验 经验
工作经历 经验
出生日期 年龄
教育背景 教育背景
教育情况 教育背景

Fig.3

Sample of Chinese resume"

Fig.4

CBOW model"

Fig.5

Results of word embedding"

Table 4

Calculated consumption of each model on Car Evaluation"

No LSTM Re-LSTM Arc-LSTM
1 0:04:14.76 0:03:58.32 0:04:18.14
2 0:05:37.39 0:05:15.26 0:05:31.86
3 0:05:02.01 0:05:16.63 0:05:25.29
4 0:05:10.52 0:05:01.50 0:05:18.12
5 0:05:01.46 0:04:57.82 0:05:02.50
6 0:05:16.82 0:05:05.30 0:05:20.50
7 0:05:13.66 0:05:03.84 0:05:16.57
8 0:05:13.38 0:05:00.40 0:05:17.63
9 0:05:19.55 0:05:13.35 0:05:21.16
10 0:05:17.39 0:04:59.50 0:05:16.65
AVG 0:05:08.69 0:04:59.19 0:05:12.84

Fig.6

ROC comparison chart"

Table 5

AUC of each model on Car Evaluation"

评判指标 LSTM Re-LSTM Arc-LSTM
AUC 0.789 1 0.799 8 0.834 4

Table 6

Calculated consumption of each model on Car Evaluation %"

评判指标 LSTM Re-LSTM Arc-LSTM
训练精度均值 77.39 77.98 79.42
测试精度均值 76.50 77.54 79.22

Table 7

Calculated consumption of each model on Adult"

No LSTM Re-LSTM Arc-LSTM
1 0:13:30.83 0:12:04.69 0:13:36.12
2 0:13:57.20 0:13:06.58 0:12:37.52
3 0:13:00.34 0:12:14.62 0:13:15.56
4 0:15:09.22 0:14:07.00 0:16:32.00
5 0:13:49.67 0:13:15.03 0:14:16.82
6 0:14:51.88 0:13:50.40 0:12:45.10
7 0:14:37.62 0:14:01.51 0:13:17.88
8 0:14:48.64 0:14:01.78 0:14:40.52
9 0:16:17.18 0:16:25.57 0:16:55.70
10 0:15:20.20 0:15:31.25 0:16:14.50
AVG 0:15:22.79 0:13:51.84 0:14:24.20

Fig.7

ROC comparison chart"

Table 8

AUC of each model on Adult"

评判指标 LSTM Re-LSTM Arc-LSTM
AUC 0.793 1 0.804 8 0.815 1

Table 9

Calculated consumption of each model on Adult %"

评判指标 LSTM Re-LSTM Arc-LSTM
训练精度均值 77.19 78.41 80.18
测试精度均值 77.12 78.23 79.74

Table 10

Calculated consumption of each model on Avila"

No LSTM Re-LSTM Arc-LSTM
1 0:59:15.31 0:57:52.23 0:58:19.54
2 0:56:59.87 0:54:82.63 0:55:09.32
3 1:12:32.65 1:11:43.98 1:12:13.55
4 0:06:21.82 1:02:51.02 1:04:02.02
5 1:06:14.37 1:02:00.46 1:05:08.46
6 0:54:07.49 0:52:07.87 0:52:29.16
7 0:53:44.59 0:51:32.24 0:56:51.12
8 0:52:35.66 0:49:16.91 0:52:32.26
9 0:56:21.39 0:54:33.67 0:55:39.54
10 1:13:36.99 1:10:53.54 1:11:43.56
AVG 1:01:17.54 0:58:49.08 0:59:69.85

Fig.8

ROC comparison chart"

Table 11

AUC of each model on Avila"

评判指标 LSTM Re-LSTM Arc-LSTM
AUC 0.742 3 0.755 2 0.773 7

Table 12

Calculated consumption of each model on Avila %"

评判指标 LSTM Re-LSTM Arc-LSTM
训练精度均值 75.66 76.69 78.94
测试精度均值 75.05 76.59 78.37

Table 13

Experimental Results of each model on Resume %"

算法 准确率 召回率 F1值
LSTM 76.68 67.26 71.21
Re-LSTM 81.41 71.43 73.51
Arc-LSTM 87.83 76.15 77.18
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