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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/76423
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/76423


    Title: 基於文件相似度的標籤推薦-應用於問答型網站
    Applying Tag Recommendation base on Document Similarity in Question and Answer Website
    Authors: 葉早彬
    Tsao, Pin Yeh
    Contributors: 楊建民
    葉早彬
    Tsao, Pin Yeh
    Keywords: 文字探勘
    標籤推薦
    群眾智慧
    Text Mining
    Tag Recommendation
    Collective Intelligence
    Date: 2015
    Issue Date: 2015-07-13 11:07:04 (UTC+8)
    Abstract: 隨著人們習慣的改變,從網路上獲取新知漸漸取代傳統媒體,這也延伸產生許多新的行為。社群標籤是近幾年流行的一種透過使用者標記來分類與詮釋資訊的方式,相較於傳統分類學要求物件被分類到預先定義好的類別,社群標籤則沒有這樣的要求,因此容易因應內容的變動做出調整。
    問答型網站是近年來興起的一種個開放性的知識分享平台,例如quora、Stack Overflow、yahoo 奇摩知識+,使用者可以在平台上與網友做問答的互動,在問與答的討論中,結合大眾的經驗與專長,幫助使用者找到滿意的答案,使用單純的問答系統的好處是可以不必在不同且以分類為主的論壇花費時間尋找答案,和在關鍵字搜索中的結果花費時間尋找答案。
    本研究希望能針對問答型網站的文件做自動標籤分類,運用標籤推薦技術來幫助使用者能夠更有效率的找到需要的問題,也讓問答平台可以把這些由使用者所產生的大量問題分群歸類。
    在研究過程蒐集Stack Exchange問答網站共20638個問題,使用naïve Bayes演算法與文件相似度計算的方式,進行標籤推薦,推薦適合的標籤給新進文件。在研究結果中,推薦標籤的準確率有64.2%
    本研究希望透過自動分類標籤,有效地分類問題。幫助使用者有效率的找到需要的問題,也能把這些由使用者所產生的大量問題分群歸類。
    With User's behavior change. User access to new knowledge from the internet instead of from the traditional media. This Change leads to a lot new behavior. Social tagging is popular in recent years through a user tag to classify and annotate information. Unlike traditional taxonomy requiring items are classified into predefined categories, Social tagging is more elastic to adjust through the content change.
    Q & A Website is the rise in recent years. Like Quora , Stack Overflow , yahoo Knowledge plus. User can interact with other people form this platform , in Q & A discussion, with People's experience and expertise to help the user find a satisfactory answer.
    This study hopes to build a tag recommendation system for Q & A Website. The recommendation system can help people find the right problem efficiently , and let Q & A platform can put these numerous problems into the right place.
    We collect 20,638 questions from Stack Exchange. Use naïve Bayes algorithm and document similarity calculation to recommend tag for the new document. The result of the evaluation show we can effectively recommend relevant tags for the new question.
    Reference: 林倩妏, and 卜小蝶. "標籤雲在圖書資訊服務之應用初探." 海峽兩岸圖書資訊學學術研討會論文集 二-117 (2010).
    陳光華,莊雅蓁. 應用於資訊檢索的中文同義詞之建構. 資訊傳播與圖書館學, 8 (1), 2001 年 9 月, 2001, 59-75.
    陸明怡. "以群眾智慧觀念為基礎之群體意見結論推論模式" (碩士論文) 清華大學(2011)
    楊玉齡譯,Surowiecki, James 原著,2005,《群眾的智慧:如何讓個人、團隊、企業 與社會變得更聰明》,台北市:遠流公司出版社。
    戴瑋. "應用社會化協同標籤於網路資源搜尋." (碩士論文)中央大學(2008).
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    Budura, A., Michel, S., Cudré-Mauroux, P., & Aberer, K. (2009). Neighborhood-based tag prediction. In The semantic web: research and applications (pp. 608-622). Springer Berlin Heidelberg.
    Cao, H., Xie, M., Xue, L., Liu, C., Teng, F., & Huang, Y. (2009). Social tag prediction base on supervised ranking model. In Proceeding of ECML/PKDD 2009 Discovery Challenge Workshop (pp. 35-48).
    Heymann, P., Ramage, D., & Garcia-Molina, H. (2008, July). Social tag prediction. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 531-538). ACM.
    Huberman, S. G. B. A. The Structure of Collaborative Tagging Systems. No. cs. DL/0508082. cs/0508082, 2005.
    Jordan, A. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 14, 841.
    Rodrigues, E. M., Milic-Frayling, N., & Fortuna, B. (2008, December). Social tagging behaviour in community-driven question answering. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 112-119). IEEE Computer Society.
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    Yin, D., Xue, Z., Hong, L., & Davison, B. D. (2010, July). A probabilistic model for personalized tag prediction. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 959-968). ACM.
    Rendle, S., Balby Marinho, L., Nanopoulos, A., & Schmidt-Thieme, L. (2009, June). Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 727-736). ACM.
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    https://archive.org/details/stackexchange
    Description: 碩士
    國立政治大學
    資訊管理研究所
    102356003
    103
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102356003
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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