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    題名: 基於糢糊關聯分析之論文推薦方法
    其他題名: Paper recommendation method based on fuzzy correlation
    作者: 林敬凱;Lin, Ching-kai
    貢獻者: 淡江大學資訊工程學系碩士班
    林丕靜
    關鍵詞: 樣本模糊相關係數;多重模糊相關係數;推薦系統;simple fuzzy correlation coefficient;multiple fuzzy correlation coefficient;Recommendation System
    日期: 2010
    上傳時間: 2010-09-23 17:33:45 (UTC+8)
    摘要: 現今社會中,大部份的文件資料都會被化為數位型式置放於網路上,因此可以把網路視為一個資料庫,而且是資料量與提供者最多的資料庫,如何去挖掘這個龐大的資料庫一直是熱門的研究主題。在廣大的資料庫中去根據使用者的行為興趣來找到最合適的文章並推薦給使用者,以有許多的各種推薦的演算法被提出來解決並各有其優缺點,本論文主要目的為解決因使用者輸入資訊不足而無法做出正確的推薦的問題。
    本研究以推薦系統的方式來幫助使用者更容易的檢索出所需的文章,以文章中的關鍵字作為代表該文章的特徵向量,透過模糊關聯係數找出文章間的關聯性,並以使用者點閱過的文章順序做為輸入資料,找出與這些使用者感興趣的文章具有高度相關的其他文章推薦給使用者。我們採取的方法從一開始先使用樣本模糊相關係數找出與使用者點選的第一篇文章相關的文章後進行推薦,接著隨著使用者點選更多文章來獲得更多使用者的輸入資料後,再利用多重模糊相關係數運算找出與這些文章關聯性高的其他可能文章並推薦,透過這樣循環式的推薦,來提高推薦的正確性。
    根據本論文的實驗證實,使用此種將模糊相關係數應用於文章推薦問題的新方法,比起用傳統計算文章相關性的方法來處理文章推薦問題更加的適合。
    Due to the growth of Internet , more and more content can be accessed on Internet. Finding articles which user may be interesting and recommend to user by user''s behavior becomes more and more important. Many kinds of algorithm for solving this problem are proposed, and each algorithm has their own advantages and disadvantages. The main purpose of this thesis is to solve problem that how to quickly finding content without enough information. We propose the fuzzy correlation used recommendation system concept to help user for retrieving the relevant articles. The article keywords are encoded as feature vectors to represent the article, we use the article keywords as feature vectors to represent the article, and find correlation between articles by fuzzy correlation coefficient. We use the article user viewed as the reference data to find the article has highly relevant for recommendation. Two steps in our proposed method to iterative recommend the more relevant article to use. First, compute and rank a correlation between the original article and the extension of the article through the simple fuzzy correlation coefficient. Second, we can get more information as reference data after the user click more articles. We use the result of last time recommendation as a new database, and then use multiple fuzzy correlation coefficient to find the articles which has highly relevant to the other article user viewed. We improve the accuracy of recommendation through the iterative recommendation step. We verify experimentally that this revised method, when used to text recommendation problem, outperforms than those methods designed for text recommendation problem.
    顯示於類別:[資訊工程學系暨研究所] 學位論文

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