應用機器學習預測求職網站會員投遞履歷之行為

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姓名

陳姵嘉(Pei-Chia Chen) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

應用機器學習預測求職網站會員投遞履歷之行為

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至系統瀏覽論文 (2027-7-18以後開放)

摘要(中)

徵才廣告多數以無差異化方式在求職網站上進行曝光，為有足夠的曝光量，可能透過求職網站上的展示型廣告增加曝光量，許多徵才企業會以收到求職履歷數作為曝光效果的衡量依據，一旦收取的履歷數過低，就可能引起客訴、補償客戶等行為，而精準廣告是增加對目標族群曝光方式之一。
本研究將以個案求職網站的角度，透過機器學習方式預測在職中求職會員在求職網站上投遞履歷之行為，並探討投遞履歷人數較多與較少的冷門、熱門時段之差異。本研究比較隨機森林、支援向量機、羅吉斯回歸與貝式分類器四種方法在預測分類上的有效與精確性，並結合主成分分析與資訊增益兩種特徵選取、萃取方式，比較兩種特徵選取在預測上的幫助。
實驗結果中於冷熱門時段與各機器學習方法組合探討下，羅吉斯迴歸、隨機森林在冷熱門時段 Macro-F1 與召回率皆為 64%以上、AUC 為 70%，三種衡量指標皆較貝氏分類器與支援向量機佳;精確率不論冷熱區間，四種模型表現皆可達 60%。
以 Macro-F1、召回率、AUC 表現較佳的羅吉斯迴歸、隨機森林更仔細地探討發現:兩種方法在冷門、與熱門時段表現趨勢皆較一致。

1. 在召回率表現中，隨機森林無投遞履歷召回率為 70%、有投遞履歷為 65%;羅吉斯迴歸無投遞履歷召回率則為 80%、有投遞履歷招回率約 40%以上，兩者差異較隨機森林大。

2. 隨機森林有投遞履歷的精確率 48%與無投遞 80%兩者差異最大，且在熱門時段下是本次研究中比較的四種模型中最低的;羅吉斯迴歸有投遞的精確率約為 50%、而無投遞履歷為 70%，表現與貝氏分類器、支援向量機相似。

摘要(英)

Most recruitment advertisements are advertised on job search websites in an imprecise way. In order to have more exposure, it is possible to increase exposure through display advertisements on job search websites for expanding their reach.
The number of job application resumes is used as the basis for measuring the exposure effect. When the customers get fewer job applications, it may cause customer complaints and compensate customers. Accurate advertising is one of the ways to increase exposure to target groups.
This study uses machine learning to predict the behavior of job seekers applying for jobs on job search websites, and explores the differences between unpopular and popular times when there are more and less people applying. Besides, this study compares the results of four algorithms, namely Random Forest, Support Vector Machine, Logistic Regression and Bayesian classifier in the prediction classification, and combines two feature selection of principal component analysis and information gain to compare the two methods which could help in prediction.
In the experimental results, the Macro-F1and the recall rate of Logistic regression and random forest in the cold and hot period are both over 64%, and the AUC are 70%. Except for the support vector machine, the precision of others is all over 60%. Furthermore, Logistic regression and random forest are consistent in the trend of unpopular and popular periods. The relevant descriptions are as follows.

1. Random forest has the recall of 70% without applied job, and 65% with applied job. Logistic regression has the recall of 80% without applied job , and 40% with applied job.

2. The precision of random forest with applied jobs is 48%, and without applied jobs is 80%. The precision of Logistic regression is similar to Bayesian classifier and support vector machine, which is 50% without applied jobs and 40%with applied jobs.

關鍵字(中)

★ 機器學習
★ 投遞履歷預測
★ 隨機森林

關鍵字(英)

★ machine learning
★ applied jobs prediction
★ random forest

論文目次

中文摘要…….………….……….……………….………….…………….………….………….I
ABSTRACT…….………….……….……………….………….…………….………….………II
誌謝…….………….……….……………….………….…………….………….………….………II
圖目錄…….………….……….……………….………….…………….………….………….…VI
表目錄…….………….……….……………….………….…………….………….……………VII
附錄表目錄…….………….……….……………….………….…………….………………VIII
一、緒論………….………….………….………….………….………….………….……………1
1-1 研究背景………….………….………….………….………….………….………….……1
1-2 研究動機………….………….………….………….………….………….………….……3
1-3 研究目的………….………….………….………….………….………….………….……7
1-4 論文架構………….………….………….………….………….………….………….……7
二、文獻探討………….………….………….………….………….………….………….……8
2-1 機器學習應用於招募領域………….………….………….……………..…………8
2-2 機器學習應用於績效與晉升領域………….………….………….………….10
2-3 機器學習應用於離職預測………….………….………….………….………….11
2-4 綜合討論………….………….………….………….………….………….…………………12
三、研究方法………….………….………….………….………….………….……….………14
3-1 資料蒐集與處理………….………….………….………….………….………….……15
3-1-1 篩選冷/熱門時段………….………….………….………….………….…………16
3-1-2 訓練/測試資料集區間…….………….……….……………….……….………16
3-1-3 資料前處理………….………….………….………….………….………….………18
3-2 模型建立與績效評估………….………….………….………….………….………19
四、研究結果與分析…….…………….…………….…………….…………………………24
4-1 原始無特徵選取下整體測試結果…….…………….…………….……………24
4-2 主成分分析與資訊增益特徵選取下測試結果…….…………….……26
4-3 各模型測試結果…….………….……….……………….………….…………….……30
4-4 冷熱區間測試結果…….………….……….……………….………….…………….32
4-5 誤置成本…….………….……….……………….………….…………….…………………34
五、結論…….………….……….……………….………..…………….………….……………36
5-1 結論…….………….……….……………….………..…………….…………….…………36
5-2 研究限制與建議…….………….……….………………..…….………….…………37
參考文獻…….………….……….……………….………….…………….………….………………38
附錄…….………….……….……………….………….…………….………….…….…….………41

參考文獻

A. 中文文獻
陳冠吟. (2017). 決策樹、羅吉斯迴歸與類神經網路預測員工績效之比較研究 [國立中央大學]. 收入人力資源管理研究所: 卷碩士. https://hdl.handle.net/11296/krcn8e
戴師勇. (2017). 員工離職預測分析之研究:以某會計師事務所審計部門為例 [東吳大學]. 收入巨量資料管理學院碩士學位學程: 卷碩士. https://hdl.handle.net/11296/3863w4 房美玉. (2020). 第 2 課招募與甄選. 收入人力資源管理的 12 堂課(全新內容經典珍藏版) (頁 p.37-38). 遠見天下文化出版股份有限公司.
黃國政. (2006). 運用文字探勘技術於人才招募推薦系統之研究 [靜宜大學]. 收入資訊管理學系研究所: 卷碩士. https://hdl.handle.net/11296/5ffn3g
黃詩涵. (2020). 運用資料探勘技術建立員工晉升之預測模型 [國立中央大學]. 收入人力資源管理研究所在職專班: 卷碩士. https://hdl.handle.net/11296/6ma266
黃同圳. (2020). 第 5 課招募與甄選. 收入人力資源管理的 12 堂課(全新內容經典珍藏版) (頁 p.148-149). 遠見天下文化出版股份有限公司.
劉清彬. (2012). 運用資料探勘於人力銀行配對機制之研究 [世新大學]. 收入資訊傳播學研究所(含碩專班): 卷碩士. https://hdl.handle.net/11296/q6s35z
徐晟熏. (2015). 資料探勘(Data mining)-在人力資源管理上的分析與應用 [國立中央大學]. 收入人力資源管理研究所: 卷碩士. https://hdl.handle.net/11296/p36932
B. 英文文獻
Almalis, N. D., Tsihrintzis, G. A., Karagiannis, N., & Strati, A. D. (2015). FoDRA — A new content-based job recommendation algorithm for job seeking and recruiting. 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), 1–7. https://doi.org/10.1109/IISA.2015.7388018
Anderson, P., & Pulich, M. (2000). Recruiting Good Employees in Tough Times. The Health Care Manager, 18(3). https://journals.lww.com/healthcaremanagerjournal/Fulltext/2000/18030/Recruiting_Good_E mployees_in_Tough_Times.5.aspx
Bao, L., Xing, Z., Xia, X., Lo, D., & Li, S. (2017). Who Will Leave the Company?: A Large- Scale Industry Study of Developer Turnover by Mining Monthly Work Report. 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR), 170–181. https://doi.org/10.1109/MSR.2017.58
Barber, A. (1998). Recruiting Employees: Individual and Organizational Perspectives. https://doi.org/10.4135/9781452243351
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
38
de Jesus, A. C. C., Júnior, M. E. G. D., & Brandão, W. C. (2018). Exploiting linkedin to predict employee resignation likelihood. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 1764–1771. https://doi.org/10.1145/3167132.3167320
Faliagka, E., Iliadis, L., Karydis, I., Rigou, M., Sioutas, S., Tsakalidis, A., & Tzimas, G. (2014). On-line consistent ranking on e-recruitment: Seeking the truth behind a well-formed CV. Artificial Intelligence Review, 42(3), 515–528. https://doi.org/10.1007/s10462-013-9414- y
Harsha, B. S., Varaprasad, A. J., & Sujith, L. V. N. P. S. (2020). EARLY PREDICTION OF EMPLOYEE ATTRITION. International Journal of Scientific & Technology Research, 9(3), 3374–3379.
Juan, C., Yong, W., & Zhang, H. (2015). Research on the effects of EAPs on turnover intentions. 2015 12th International Conference on Service Systems and Service Management (ICSSSM), 1–5. https://doi.org/10.1109/ICSSSM.2015.7170158
Kirimi, J. M., & Moturi, C. A. (2016). Application of data mining classification in employee performance prediction. International journal of computer applications, 146(7), 28–35.
Liu, J., Long, Y., Fang, M., He, R., Wang, T., & Chen, G. (2018). Analyzing Employee Turnover Based on Job Skills. Proceedings of the International Conference on Data Processing and Applications, 16–21. https://doi.org/10.1145/3224207.3224209
Long, Y., Liu, J., Fang, M., Wang, T., & Jiang, W. (2018). Prediction of Employee Promotion Based on Personal Basic Features and Post Features. Proceedings of the International Conference on Data Processing and Applications, 5–10. https://doi.org/10.1145/3224207.3224210
McKenna, E. F., & Beech, N. (1995). The essence of human resource management. Prentice Hall.
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290. https://doi.org/10.1016/j.dss.2020.113290
Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. https://www.microsoft.com/en-us/research/publication/sequential-minimal- optimization-a-fast-algorithm-for-training-support-vector-machines/
Qian, X., & Ohwada, H. (2018). Application of Data Mining Classification in Job-Changing. Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 107–110. https://doi.org/10.1145/3195106.3195136
Robbins, S. P., & Judge, T. A. (2017). Essentials of Organizational Behavior, eBook, Global Edition (14 本). Pearson Education. https://books.google.com.tw/books?id=5x8uDwAAQBAJ Saradhi, V. V., & Palshikar, G. K. (2011). Employee churn prediction. Expert Systems with Applications, 38(3), 1999–2006. https://doi.org/10.1016/j.eswa.2010.07.134
39

Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Siting, Z., Wenxing, H., Ning, Z., & Fan, Y. (2012). Job recommender systems: A survey. 2012 7th International Conference on Computer Science & Education (ICCSE), 920–924. https://doi.org/10.1109/ICCSE.2012.6295216
Sudha, G., K, S. K., S, S. J., D, N., S, S., & G, K. T. (2021). Personality Prediction Through CV Analysis using Machine Learning Algorithms for Automated E-Recruitment Process. 2021 4th International Conference on Computing and Communications Technologies (ICCCT), 617–622. https://doi.org/10.1109/ICCCT53315.2021.9711787
Suthaharan, S. (2016). Support Vector Machine. 收入 S. Suthaharan (編輯), Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning (頁 207–235). Springer US. https://doi.org/10.1007/978-1-4899-7641-3_9 Webb, G. I. (2010). Naïve Bayes. 收入 C. Sammut & G. I. Webb (編輯), Encyclopedia of Machine Learning (頁 713–714). Springer US. https://doi.org/10.1007/978-0-387-30164- 8_576

指導教授

柯士文(Shi-Wen Ke)

審核日期

2022-9-15

推文