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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52342

    Title: 基於糢糊關聯分析之論文推薦方法
    Other Titles: Paper recommendation method based on fuzzy correlation
    Authors: 林敬凱;Lin, Ching-kai
    Contributors: 淡江大學資訊工程學系碩士班
    Keywords: 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
    Date: 2010
    Issue Date: 2010-09-23 17:33:45 (UTC+8)
    Abstract: 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。
    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.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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