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論文中文名稱:長期照護人才推薦系統之研究 [以論文名稱查詢館藏系統]
論文英文名稱:A Study of Recommendation System on Recruiting in Long-Term Care [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與財金管理系碩士班
畢業學年度:105
畢業學期:第二學期
出版年度:106
中文姓名:吳冠儀
英文姓名:Kuan-Yi Wu
研究生學號:104AB8014
學位類別:碩士
語文別:中文
口試日期:2017/06/28
論文頁數:47
指導教授中文名:翁頌舜
指導教授英文名:Sung-Shun Weng
口試委員中文名:林宜隆;吳瑞堯
中文關鍵詞:文字探勘長期照護人才推薦推薦系統
英文關鍵詞:Text MiningLong-Term CareTalents RecommendationRecommended System
論文中文摘要:現代社會結構改變,老年人口數量逐年遞增,成為各個國家需特別注意的社會現象。台灣已在1993年進入高齡化社會,預計2025年會進入超高齡社會。許多研究報告顯示長期照護人力大多呈現不足。根據國家發展委員會所發佈歐美先進國家因應長照服務人力不足之作法,其中為強化就業媒合機制,目前國內企業在人力銀行求才時,履歷順序多以履歷更新日期為主,列表資訊繁雜,仍需一一檢視結果內容,使企業仍要花時間去篩選。本研究透過爬蟲程式蒐集人力銀行公開履歷,利用Word2Vec技術訓練語言模型,並利用K-means分群與協同過濾法於網頁平台選擇條件後,推薦出Top-5履歷。經實驗評估後,本研究的推薦結果,其F-measure達74.81%,由實驗結果可觀察到,該系統擁有不錯的準確度。使用此系統可增加企業徵才之效益,更可減少搜尋浪費的多餘時間成本。
論文英文摘要:Nowadays, modern social structure changes and older population increases in all countries every year. This problem of social phenomena needs to pay particular attention. In 1993, Taiwan has entered the aging society, it is expected that it will enter into super-aged society in 2025. Many studies show that long-term care of manpower is mostly insufficient. In accordance with the mechanism dealing with the shortage of long-term care of manpower in the advanced countries of Europe, there are practices to strengthen the mechanism for employment media. The current domestic enterprises seeking to manpower bank still have to spend extra time to filter the employee’s vitae. This study uses web crawler to gather information on the Human Resource Agency. This study uses Word2Vec training language model and the K-means and Collaborative Filtering algorithms for major research methods. After people choosing the requirement conditions on the web site platform, this study recommends the Top-5 vitae. After experiments, the F-measure value of the recommendation results is 74.81%. It can be observed that this system has good accuracy by the experimental results. The result of this study shows that our proposed system can increase the effectiveness of the enterprise recruitment as well as reduce the search time.
論文目次:摘 要 i
ABSTRACT ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究流程 4
第二章 文獻探討 6
2.1 長期照護(Long-Term Care) 6
2.2 人力資源(Human Resource) 7
2.3 詞向量(Word Vectors) 7
2.4 推薦系統(Recommender System) 9
2.4.1 內容導向式推薦(Content-based Recommendations) 9
2.4.2 協同過濾式推薦(Collaborative Filtering Recommendations) 10
2.4.3 混合式推薦(Hybrid Recommendations) 11
第三章 研究方法 13
3.1 系統架構 13
3.2 資料蒐集 14
3.2.1 履歷資料 15
3.2.2 職缺資料 15
3.3 模組功能說明 16
3.3.1 職缺模組 16
3.3.2 履歷處理模組 18
3.3.3 文字雲模組 21
3.3.4 推薦模組 22
第四章 實驗結果與分析 25
4.1 實驗環境 25
4.2 實驗數據 25
4.3 實驗設計 29
4.3.1 職缺模組 29
4.3.2 履歷處理模組 30
4.2.3 文字雲模組 31
4.2.4 推薦模組 32
4.4 實驗評估方法 34
4.4.1 精確率(Precision) 34
4.4.2 召回率(Recall) 35
4.4.3 F度量(F-measure) 36
4.5 實驗結果 36
4.5.1 精確率(Precision)評估結果 37
4.5.2 召回率(Recall)評估結果 39
4.5.3 F度量(F-measure)評估結果 40
第五章 結論 42
5.1 研究結論與貢獻 42
5.2 研究限制與未來展望 43
參考文獻 44
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