本研究以自然語言處理技術為基礎，運用文字探勘技術分析應聘者履歷資料，使用python進行程式撰寫，對履歷資料進行評分排序，構建履歷推薦系統，實現履歷篩選的智能化。整個研究過程分為兩個實驗階段進行，先導實驗階段主要為模擬實際履歷篩選狀況，比對系統的履歷自動化篩選、模擬HR組和模擬主管組的篩選結果；正式實驗階段將系統的履歷自動化篩選結果與實際招募狀況結果對比，計算吻合度。
先導實驗中有84.09%的「系統評」結果比「模擬HR評」更接近「模擬主管評」的順序。正式實驗中履歷評分推薦系統篩選結果與實際企業人工履歷篩選狀況吻合度平均高達80.8%。
本研究在履歷篩選推薦上作法與過往相關研究較為不同的方面為本研究建構之系統注重個人化篩選，可以遵照個人化需求條件進行篩選，在企業中可以靈活快速應對多項不同篩選條件之職缺；待企業熟悉履歷推薦系統之使用，可更進一步在篩選過程中把應聘者進行不同職缺間的篩選，藉此進行更加多元、主動的人崗匹配推薦，做到適才適所，協助應聘者媒合更廣闊且合適之職務選擇。

This research employs the Natural Language Processing (NLP) technology, which involves the application of text mining. The experimental process uses python for programming. This technique aims to analyze the resume data and construct the resume recommendation system. The entire research divides into two experiments. The pilot experiment simulates the actual resume selection process. The pilot experiment involves comparing the results of ‘Simulated Supervisor’ with the resume recommendation system and ‘Simulated HR’ respectively. The formal experiment focuses on comparing the results of the automated selection of the system's resume with the actual recruitment status and calculating the inter-rater agreement.
In the pilot experiment, 83.36% of the ‘System Review’ results are close to the order of ‘Simulated Supervisor’ than the ‘Simulated HR’. In the formal experiment, the result of resume recommendation system tallies with the actual enterprise's manual resume selection status on average up to 80.8%.
This research focuses on personal preference in the way of resume selection and recommendation, which is quite different from the previous related research. The system will satisfy many different selection conditions in the enterprise flexibly and quickly. In the selection process, candidates can be selected for different job vacancies. This is a person-job fit recommendation, it can achieve the right fit and assist candidates to match more suitable job choices.

REFERENCE
1.Barber, AE. (1998). Recruiting Employees: Individual and Organizational Perspective, Thousand Oaks, CA: Sage.
2.Liu, B. (2012). Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
3.Breaugh, J.A. (1992). Recruitment: Science and Practice. Boston, MA: PWS-KENT Publishing Company.
4.Cable, DM. and Turban, DB. (2001). Establishing the Dimensions, Sources and Value of Job Seekers Employer Knowledge During Recruitment, Research in Personnel and Human Resources Management, 20, 115-163.
5.Carlson, K. D. and Connerley, M. L. (2003). The staffing cycle framework: viewing staffing cycle as a system of decision events, Journal of Management, 29(1), 51-78.
6.Chapman, D. and Webster, J. (2003), The Use of Technologies in Recruiting, Screening, and Selection Processes for Candidates, International Journal of Selection and Assessment, 11(2/3), 113-20.
7.Jou, C. (2013). Spam e-mail classification based on the IFWB algorithm, Proceedings of the 5th Asian conference on Intelligent Information and Database Systems (ACIIDS), 1, 314-324.
8.Wang, CC. and Chen, SY. (2007), Using header session messages to anti-spamming. Computers and Security, 26(5), 381-390.
9.Cole, M.S., Rubin, R.S., Field, S.H. and Giles, W.F. (2007). Recruiters’ perceptions and use of applicant resume information: screening the recent graduate, Applied psychology: An International Review, 56(2), 319-343.
10.De Meo, P., Quattrone, G., Terracina, G. and Ursino, D. (2007). An XML-based multiagent system for supporting online recruitment services, Systems, Man and Cybernetics, Part A: Systems and Humans, 37,464-480.
11.Faliagka, E., Tsakalidis, A. and Tzimas, G. (2012). An Integrated E-Recruitment System for Automated Personality Mining and Applicant Ranking, The Seventh International Conference on Internet and Web Applications and Services, 22(5).
12.Gatewood, R. D., Field, H. S., and Barrick, M. (2015). Human Resource Selection (8th ed.). Boston, MA: Cengage Learning.
13.Heneman, H. G. and Judge, T. (2008). Staffing Organization, McGraw-Hill/Irwin.
14.Holm, A. B. (2012). E-recruitment: Towards a Ubiquitous Recruitment Process and Canadidate Relationship Management. Aarhus V: Rainer Hampp Verlag.
15.Järvelin, K., and Kekäläinen, J. (2000). IR evaluation methods for retrieving highly relevant documents. Paper presented at the Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 41-48. ACM: New York.
16.Ramantas, K. and Tzimas, G. (2012). Application of Machine Learning Algorithms to an online Recruitment System, The Seventh International Conference on Internet and Web Applications and Services.215-220.
17.Laumer, S. and Eckhardt, A. (2009). Help to find the needle in a haystack: integrating recommender systems in an it supported staff recruitment system, SIGMIS CPR ’09: Proceedings of the Special Interest Group on Management Information System’s 47th Annual Conference on Computer Personnel Research, ACM, New York, NY, USA.
18.McCrae, R. R., Costa Jr., P. T., and Busch, C. M. (1986). Evaluating Comprehensiveness in Personality Systems: The California Psychological Inventory and the Five-Factor Model. Journal of Personality, 61, 1-26.
19.Miner. B. and Crane, D.P. (1995). architecture：Toward a theory of human capital allocation and development. NY: Harpercollins College Publisher.
20.Archak. N, A., Ghose. A, and Ipeirotis. P.G. (2007). Show me the money: deriving the pricing power of product features by mining consumer reviews, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 56-65.
21.Raghavan, P., Fosler-Lussier, E., Elhadad, N. and Lai, A. (2014). Cross-narrative temporal ordering of medical events. Proc ACL 2014, 998-1008.
22.Pande, S. (2011). E-recruitment creates order out of chaos at SAT telecom: system cuts costs and improves efficiency, Human Resource Management International Digest, 19(3), 21-23.
23.Parry, E. and Tyson, S. (2009). What is the potential of e-recruitment to transform the recruitment process and the role of the resourcing team? Bondarouk, T., Ruel, H., Guiderdoni-Jourdain, H. and Oiry, E. (Eds): Handbook of Research on E Transformation and Human Resources Management Technologies: Organizational Outcomes and Challenges, 202-217.
24.Bobko, P., Roth, PL. and Buster, MA. (2007). The Usefulness of Unit Weights in Creating Composite Scores: A Literature Review, Application to Content Validity, and Meta-Analysis, Organizational Research Methods, 10, 689-809.
25.Biyani, P., Caragea, C., Mitra, P. and Yen, J. (2014). Identifying Emotional and Informational Support in Online Health Communities, Proc COLING 2014 Sept, 827-36.
26.Prathima, M and Sheelan, M. (2012). The Green Revolution in Human Resource Management, Asia Pacific Journal of Management & Entrepreneurship Research; Bangalore, 1(3), 227-237.
27.Kessler, R., Torres-Moreno J. and El-Beze, M. (2017). E-Gen: automatic job offer processing system for human resources. Proc. of MICAI' 07, Springer-Verlag, Berlin, Heidelberg, 985-995.
28.Roth, P.L. and Bobko, P. (2000). College grade point average as a personnel selection device: ethnic group differences and potential adverse impact, Journal of Applied Psychology, 85, 399-406.
29.Ryan, A. M. and Ployhart, R. E. (2000). Applicant’s perception of selection procedures and decisions: a critical review and agenda for the future. Journal of Management, 26(3), 565-606.
30.Rynes. (1991). The importance of recruitment in job choice: a different way of looking, Personnel Psychology, 44(3), 487-521.
31.Amdouni, S. and Ben Abdessalem Karaa, W. (2010). "Web-based recruiting;Framework for CV structuring". ACS/IEEE International Conference on Computer Systems and Applications. HAMMAMET TUNISIA, 16-19.
32.Savitz, A., and Weber, K. (2013). Talent, transformation, and the triple bottom line: How companies can leverage human resources to achieve sustainable growth. John Wiley & Sons.
33.Senthil Kumaran, V. and Sankar, A. (2013). Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT), Int. J. Metadata, Semantics and Ontologies, 8(1), 56–64.
34.Sun, S., Luo, C. and Chen, J. (2017). A review of natural language processing techniques for opinion mining systems, Information Fusion, 36, 10-25.
35.SIMON, HA. (1947). Administrative behavior: A Study of Decision making Processes in Administrative Organization, 1st ed., New York: The Macmillan Company.
36.Squires, A. (2008). International recruitment: Many faces, one goal-part 2. Nursing Management; Chicago, 39(10), 18.
37.McCrae, RR., Costa Jr, PT., and Busch, CM. (2006). Evaluating comprehensiveness in personality Systems: The California Q-set and the Five-Factor Model. Journal of Personality, 54(2), 430~446.
38.Tsai, W.C., Yang, W.F., and Suen, H.Y. (2005). Exploring the dimensionality of corporate image and its linkage to organizational attractiveness. Paper presented at the Academy of Management Annual Meeting, Honolulu, Hawaii, USA.
39.Tumasjan, A., Sprenger, T.O., Sandner, P.G. and Welpe, I.M. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment, Proceedings of the Fourth International Conference on Weblogs and Social Media, 178-185.
40.Radevski, V. and Trichet, F. (2006). Ontology-Based Systems Dedicated to Human Resources Management: An Application in e-Recruitment, On the Move to Meaningful Internet Systems, 4278, 1068-1077.
41.Karaa, WBA. and Mhimdi, N. (2011). Using ontology for resume annotation. International Journal of Metadata Semantics and Ontologies, 6(4), 166-174.
42.Weathington, B. L., and Bechtel, A. R. (2012). Alternative Sources of Information and the Selection Decision Making Process. Journal of Behavioral & Applied Management, 13(2), 108-120.
43.Strange, W. (2012). Interview with Lyssa Barber, Managing Consultant. Human Resource Management International Digest; Bradford, 20(2), 39-41.
44.Yahiaoui, L., Boufaida, Z. and Prie, Y. (2006). Semantic Annotation of Do cunents Applied to e-recruitment, Proceedings of SWAP 2006 the 3rd Italian Semantic Web WoIkshop, Pisa, Italy.
45.Gupta, Y. (2016). Literature review on E-Recruitment: A step towards paperless HR’, International Journal of Advance Research in Computer Science and Management Studies, 4(1), 79-84.

Website Citation:
1.BOST. (2018), from https://www.bost.ey.gov.tw/Default.aspx
2.BUSINESS NEXT. (2018), BRILENT, from https://www.bnext.com.tw/article/42535/brilent-ai-help-hr-filter-resume
3.FINET.HK. (2018), Liepin, from https://www.finet.hk/Newscenter/news
4.Google AI Blog. (2018), Deep Learning for Electronic Health Records, from https://ai.googleblog.com/2018/05/deep-learning-for-electronic-health.html
5.IBM. (2017), Watson, from https://www.ibm.com/jp-ja/
6.We Are Social. (2018), Global Digital Report 2018, from https://digitalreport.wearesocial.com
7.MIDASITNEWS. (2018), Artificial Intelligence (AI) Interview, "Facial Expression, Voice, EEG Analysis", from http://midasit.com/public/news