基於文字履歷及人格特質應用機械學習改善錄用品質

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黃敬堯(Ching-Yao Huang) 查詢紙本館藏

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資訊管理學系在職專班

論文名稱

基於文字履歷及人格特質應用機械學習改善錄用品質

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摘要(中)

進入後疫情時代世界正面臨生活型態轉型，5G、AI、車用電子等半導體相關需求比過去更為強烈，台灣適逢半導體產業的優勢，擁有完整的IC設計、晶圓代工、封裝測試的密集供應鏈，劇烈的環境改變帶來了更多機會，也帶來了更多產業拓展的需求。在大環境蓬勃發展的情況中，企業面臨競爭著不僅是自身的技術力，更一部分是生產量能擴張的壓力，於此，大量的人才錄用及培育便成了當前企業的一大考驗，能快速地獲得適任人材並培訓，縮短面試所需時間、減少不適任任用的風險所花費的成本就顯著重要。本研究透過履歷資料並佐以應試者人格特質做為基礎，目的在於僅用單純的基礎資料即可辨識出該員是否為適才適任之人員，資料來源可透過現有網路資料庫或於面試前增加應試人格特質測試，不透過人為面試的主觀意見做為適任判斷，以此提供客觀評斷標準，加速面試前篩選、面試後評估之佐證。

摘要(英)

The way we used be living is different after COVID-19. It is stronger demand in semiconductors about 5G, AI, automobile. Taiwan takes this advantage with complete industrial chain about IC design, foundry, packing and testing. Opportunities come from environment changing and also requirement do. In the time, enterprise must keep technology leading and expand the capacity of production simultaneously. The recruitment and training are the problems to overcome. How to shorten the recruitment period and select competent applicants is more important. The article focuses on selecting the competent applicants base on the data of screening resumes and personal traits. These features are more objective and could be tested by equal standard. Less human judgement could reduce the bias from interviewer and decrease the time of recruitment stage.

關鍵字(中)

★ 機械學習

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
一、緒論 1
1-1.研究背景 1
1-2.研究動機 3
1-3.研究貢獻 4
二、文獻探討 5
2-1.企業徵才 5
2-2.選才 6
2-2-1.選才流程與重要性 6
2-2-2.履歷表 7
2-2-3.專業技能與心理測試 8
2-2-4.面談甄選 9
2-3.人格特質與績效 9
2-3-1.五大人格特質 (Big Five Personality Traits) 9
2-3-2.特質活化理論 (Trait Activation Theory) 11
2-3-3.人格特質與工作關係 12
2-4.資料探勘在選才的應用 14
三、個案公司背景介紹 16
3-1.個案公司背景 16
3-2.員工進用程序 16
3-3.員工績效衡量方法 17
3-4.資料與問題 18
四、資料分析方法與工具 20
4-1.分析方法 20
4-1-1.Decision Tree 20
4-1-2.Ada Boost 21
4-1-3.Random Forest 22
4-1-4.Extra Trees 23
4-1-5.Gradient Boosting 23
4-1-6.Light Gradient Boosting 24
4-1-7.K Nearest Neighbors 24
4-1-8.Logistic Regression 25
4-1-9.Linear Discriminant Analysis 25
4-1-10.Quadratic Discriminant Analysis 26
4-1-11.Ridge Regression 27
4-1-12.Naive Bayes 27
4-1-13.SVM 28
4-2.分析工具 29
4-2-1.Python 3.8.4 29
4-2-2.Pycaret 2.0 29
五、研究內容與方法 30
5-1.研究設計與流程 30
5-2.分析資料來源 32
5-2-1.履歷表 32
5-2-2.員工績效及標籤分類級距 33
5-2-3.適性問卷 35
5-3.前處理 36
5-4.訓練分類準確度 38
六、結果與分析 39
6-1.資料及統計分析 39
6-1-1.工作資料相關 39
6-1-2.應徵來源 41
6-1-3.前任工作離職原因 43
6-2.資料集訓練結果比較 45
6-2-1.原始訓練集 45
6-2-2.增加適性問卷資料 46
6-3.訓練結果檢定 47
6-4.特徵重要度 48
七、結論與建議 49
7-1.研究結論 49
7-2.研究限制 50
7-3.研究貢獻 51
7-4.未來研究建議 51
參考文獻 52

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指導教授

陳彥良

審核日期

2022-6-8

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