根據2010年人力銀行調查54%的尋找人才是以「工作分析」為主要依據，可見人才的遴選仍以經歷為主要因素。而近年來社會網絡與推薦系統普為應用於人才之找尋。 本研究實際以某國立大學EMBA學員資料，以同學與同事關係建置一個人脈社會網絡之人才推薦系統。本系統能依據使用者所輸入之人才搜尋條件，藉由距離相似度之運算，找出最接近的所需人才，並依距離相似度排序。其次，本系統可由各成員學歷，工作經歷所在之產業別，以及在組織中任職之功能別，來呈現人才之專業輪廓（Professional Profile），以作為決策者在遴選人才之依據。並提供所有關係路徑，以利使用者可進一步的諮詢路徑上成員對於推薦人選之評價。 本研究針對該校EMBA學員共計2,121人，應用資料探勘中群集分析建立推薦系統，有別於一般以關鍵字比對的搜尋方式，能找出與使用者需求條件相似度高的人才；並藉由人脈社會網路路徑，幫助使用者藉由自身的人脈評估推薦的結果。最後，本研究並提出結論、建議以及未來研究方向。 / Abstract According to the Job Bank survey in 2010, about 54% recruiters who search for specialist is mainly based on job analysis. This research is based on Social Network and Rcommendation system to build a relationship between the students and the colleagues with the personnel social network contacts, thus, a specialist recommendation system is constructed. First the system can compute the dissimilarity between the conditions users input and the background of people, find out the closest result required by sorting of similarity. Secondly, the professional profiles is established by the education background and work experiences (contain the various industries and position type), to serve as the basis for decision-makers in the selection of specialist. Besides, they can also inquire people from social network path for further appraisals of the candidate. The research is based on EMBA students totaled 2121 people, applying cluster analysis of data mining to build up the recommendation system, opposite to using key-word matching as a way to search people. Thus, the study can find the highest similar conditions demand of input. Via the associated social networks paths, to help users identify and use their own network to assess the recommend candidates. Finally, this study proposes conclusions, recommendations and future research directions. Keywords: Social Network , Similarity , Professional Profile , Specialist Recommendation System , Social Network Path

「人」是公司中最重要的資產，而在知識密集的行業中，這樣的資產更顯得重要。由於網路技術的出現，網路人力銀行也成為另外一種人才招募的新興管道，但透過網路人力銀行所召募的人才素質並沒有傳統上透過公司員工推薦進來的人才可更進一步瞭解的好處。因此本研究透過一網路人才推薦信任制度，來加強線上人力銀行之人才篩選能力，希望透過此制度能繼續保有網路人力銀行在人才招募速度上的優勢，並能加強其篩選的能力。 本研究針對人才招募管道進行了文獻的探討，提出一人才推薦制度，以某國立大學EMBA之人才庫，透過成員間的學經歷背景相似度，推薦出擁有相同顯性工作能力的人才。讓人才招募單位可以得到推薦的人才，並可對其作信任評價的推論。接著利用實驗來求出雛型系統的一些關鍵參數，讓雛型系統運作得更完善以及更符合使用者的需求。 本雛型系統結合了網路人力銀行人才招募方式可快速地招募到大量員工的特點，及員工推薦人才招募方式可招募到更適切員工的特點。並透過FOAF格式的使用，將線上社會網絡的資料格式統一，有助於縮短整個人才信任推薦系統的建立時間。 / "Human Resource" is one of the most important assets of company, especially in knowledge-intensive industries. As network technologies developed, commercial job site has also become another kind of recruitment channel. But through this kind of channel, companies don’t have better chance to know new employee than traditional way. Therefore this study filters new employees by a Recommendation & Trust Inference mechanism. Hope that commercial job site would continue to keep the advantages of high efficiency in recruitment, and enhance its filtering capability at the same time. First, this study surveys literatures in recruitment channels. And it proposes a Recommendation & Trust Inference mechanism using a national university EMBA program member data as an example. The Recommendation mechanism recommend candidates having the same specialty by comparing their similarity of education and work experience. Furthermore, recruitment unit could use Trust Inference mechanism to get suitable candidates. Third, we conduct experiments to find the key parameters for the prototype system. Make the system able to work better and meet users’ needs. The prototype system combines the benefit of commercial job site which can quickly recruit a large number of employees and the feature providing more appropriate candidates for the company recommended by staff. Simultaneously by taking use of the FOAF format, we can unify the data format in online social network. The way mentioned above will effectively reduce the system set-up time.