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研究生:陳柏翰
研究生(外文):Po-Han Chen
論文名稱:個人化線上求職推薦系統之研究
論文名稱(外文):A Personalized On-line Job Matching Recommender System
指導教授:黃謙順黃謙順引用關係
指導教授(外文):Chein-Shung Hwang
學位類別:碩士
校院名稱:中國文化大學
系所名稱:資訊管理研究所碩士在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:68
中文關鍵詞:資料探勘群集分析內容導向過濾線上求職
外文關鍵詞:data miningcluster analysiscontent-based filteringonline job hunting
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  近年來由於線上求職系統的出現,打破了多年來只能由傳統的報章雜誌媒體來進行求職的單一管道限制,也因此改變了求職者對於求職媒體管道的使用習慣。由於線上求職系統提供了更豐富、更多元的求才資訊,吸引了許多求職者的喜愛,也改變了求職媒體的生態,使得許多的求職者愈來愈仰賴以線上求職系統來找尋符合本身需求的工作機會。雖然線上求職系統具備了快速、方便、成本低廉等優點,且線上求職系統還能提供許多傳統求職方式所無法匹敵的功能,例如:線上刊登履歷、線上應徵工作等,但因龐大的求職、求才資料庫所帶來的致命傷,造成媒合機制無法保證其媒合結果的素質。

  本研究將以國內某知名人力銀行求職與求才資料庫為對象,應用資料探勘(Data Mining)中內容導向過濾(Content-Based Filter-ing)及群集分析(Cluster Analysis) 等技術進行實證分析,藉以探討如何有效地運用資料探勘的技術從大量的求職與求才資料庫中挖掘出完整媒合資訊,以推薦適當的個人化媒合資訊給使用者。
  In recent years, due to development of on-line job matching systems, looking for a job is no longer limited to follow the traditional way of getting related information from newspapers and magazines. By providing rich kinds of information, the on-line job matching systems have attracted more and more of job seekers. The on-line job match-ing system are fast, convenient, and low cost, and offers function of publishing resumes and applying for job online which traditional job-finding service can not provide. How-ever, a huge and powerful database is then needed to keep the records of available va-cancies and applicants and provide precisely matching results.

  Based on the data from a famous domestic manpower bank, this study uses con-tent-based filtering and cluster analysis techniques popular in the data mining field to verify the matching results. This paper describes how to obtain complete matching in-formation from job matching database and recommend adequate personalized matched results to users.
中文摘要 ..................... iii
英文摘要 ..................... iv
誌謝辭  ..................... v
內容目錄 ..................... vi
表目錄  ..................... viii
圖目錄  ..................... ix
第一章  緒論................... 1
  第一節  研究背景............... 1
  第二節  研究動機............... 2
  第三節  研究目的............... 4
  第四節  研究範圍與限制............ 6
  第五節  研究步驟............... 6
第二章  文獻探討................. 8
  第一節  推薦系統與電子商務.......... 8
  第二節  群集分析............... 20
  第三節  個人化服務.............. 23
  第四節  網路招募............... 27
第三章  研究方法................. 30
  第一節  研究架構............... 30
  第二節  分群模組............... 31
  第三節  履歷推薦模組............. 33
  第四節  職缺推薦模組............. 35
  第五節  資訊整合模組............. 37
  第五節  現行系統推薦流程........... 38
第四章  實作與分析................ 40
  第一節  資料預處理.............. 40
  第二節  實作環境............... 41
  第三節  評估因子............... 42
  第四節  程式實作............... 44
  第五節  結果與分析.............. 51
第五章  結論與未來研究方向............ 57
  第一節  結論................. 57
  第二節  未來研究方向............. 58
  第三節  貢獻................. 59
參考文獻 ..................... 61
附錄 ....................... 65
中文文獻:
蔡淑如 (2003). 八月份上網求職人口為347萬人, http://www.find.org.tw/0105/news/0105_news_disp.aspx?news_id=2847,資策會FIND網站。
陳世運 (2000). 台灣B2C電子商務個案探討(八)人力網站--- 104人力銀行, http://www.find.org.tw/0105/trend/0105_trend_disp.asp?trend_id=1104,資策會FIND網站。
廖婉菁(2002),「應用協同過濾機制於商品推薦之研究-以手機網站為例」,碩士論文,中原大學資訊管理學系,中壢。
英文文獻:
Adomavicius G, Tuzhilin A. (2003), "Recommendation technologies: Survey of current methods and possible extensions", IEEE Transactions on Knowledge and Data Engineering, New York, working paper 0329.
Anderson, P., Pulich, M. (2000), "Recruiting Good Employees in Tough Times", College of Business and Economics, pp. 32-40.
Anonymous. (2000), "Online recruiting: What works, what doesn't," HR Focus, New York, Iss. 00-3, pp. 11-15.
Balabanović M, Shoham Y. (1997), "Fab: content-based, collaborative recommendation", Communications of the ACM, Vol. 40, Iss. 3, pp. 66-72.
Billsus D, Pazzani M.J. (1998), "Learning Collaborative Information Filters", Proceedings of the Fifteenth International Conference on Machine Learning, pp. 46-54.
Bradley K, Smyth B, C.W. Ltd. (2002), "Personalized information ordering: A case study in online recruitment", Proceedings of the Twenty-second SGAI International Conference on KnowledgeBased Systems and Applied Artificial Intelligence, Cambridge, UK.
Bradley K, Smyth B. (2001), "Improving Recommendation Diversity", Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, pp. 85-94.
Dean, R. (1998), "personalizing your web site", available at http://builder.cnet.com/webbuilding/pages/Business/Personal/.
Deshpande M, Karypis G. (2004), "Item-Based Top-N Recommendation Algorithms", ACM Transactions on Information Systems (TOIS), Vol. 22 , Iss. 1, pp. 143-177.
Goldberg D, Nichols D, Oki BM, Terry D. (1992), "Using collaborative filtering to weave an information tapestry", Communications of the ACM, Special issue on information filtering, Vol. 35, Iss. 12, pp. 61-70.
Han, J. and Kamber, M. (2000), Data Mining: Concepts and Techniques, Morgan Kaufmann, Inc.
Wang J.C., Lin J.P. (2003), "Are Personalization Systems Really Personal? -- Effects of Conformity in Reducing Information Overload", HICSS 36.
Bradley K, Rafter R, Smyth B. (2000), "Case-Based User Profiling for Content Personalisation", Lecture Notes In Computer Science, Vol. 1892, pp. 62-72.
Piturro M. (2000), "The Power of e-Cruiting," Management Review, New York, Vol. 89, Iss. 1, pp. 33-37.
Rafter R, Bradley K, Smyth B. (1999), "Passive Profiling and Collaborative Recommendation", Proceedings of the 10th Irish Conference on Artificial Intelligence and Cognitive Science, Cork, Ireland.
Rafter R, Bradley K, Smyth B. (2000), "Automated Collaborative Filtering Applications for Online Recruitment Services", Lecture Notes In Computer Science, London, Vol. 1892, pp. 363-368.
Rafter R, Bradley K, Smyth B. (2000), "Personalised retrieval for online recruitment services", Proceedings of the 22nd Annual Colloquium on Information Retrieval, Cambridge, UK.
Rafter R, Smyth B. (2001), "Passive Profiling from Server Logs in an Online Recruitment Environment", Proceedings of the IJCAI Workshop on Intelligent Techniques Intelligent Techniques for Web Personalisation (ITWP 2001) Seattle, Washington, USA.
Sarwar B.M., Karypis G, Konstan J.A., Reidl J. (2001), "Item-based Collaborative Filtering Recommendation Algorithms", In Proc. of the 10th International World Wide Web Conference (WWW10), Data Mining and Knowledge Discovery, Hong Kong.
Sarwar B.M., Karypis G, Konstan J.A., Riedl J. (2000), "Analysis of recommendation algorithms for e-commerce", Electronic Commerce, Minneapolis, Minnesota, United States, pp. 158-167.
Schafer J.B., Konstan J.A., Riedl J. (1999), "Recommender Systems in E-Commerce", Proceedings of the 1st ACM conference on Electronic commerce, Denver, Colorado, United States, pp. 158-166.
Smith B, Linden G, York J. (2003), "Amazon.com Recommendations Item-to-Item Collaborative Filtering", Internet Computing, IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80.
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