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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/51209
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/51209


    Title: 建構人脈社會網絡人才推薦系統之研究-以某國立大學EMBA人才庫為例
    Social network-based specialist recommendation system- a case of national university EMBA datebase
    Authors: 呂春美
    Lu, Chun-Mei
    Contributors: 楊建民
    呂春美
    Lu, Chun-Mei
    Keywords: 社會網絡
    距離相似度
    專業輪廓
    人才推薦系統
    關聯路徑
    Social Network
    Similarity
    Professional Profile
    Specialist Recommendation System
    Social Network Path
    Date: 2010
    Issue Date: 2011-10-05 14:32:40 (UTC+8)
    Abstract: 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
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    Description: 碩士
    國立政治大學
    資訊管理研究所
    96356006
    99
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0096356006
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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