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    題名: 學習元件評比暨動態週期性個人化推薦機制
    其他題名: An evaluation and dynamic period personalized recommendation mechanism for learning objects
    作者: 黃俊維;Huang, Chun-Wei
    貢獻者: 淡江大學資訊工程學系碩士班
    趙榮耀;Chao, Louis R.Y.
    關鍵詞: 數位學習;資源檢索;社群理論;個人化推薦;E-Learning;information receive;social theory;Personalized Recommendation
    日期: 2011
    上傳時間: 2011-12-28 18:58:53 (UTC+8)
    摘要: 隨著網際網路的快速發展,數位學習的資源在網路中也持續遽增,使用者雖有更豐富的資源可以運用,且能利用檢索引擎便利得尋找資源,但卻常需要過濾龐大的檢索結果,才能找到其所真正需求的資源。因此本篇論文主要目的在於開發一個檢索服務,讓使用者能快速找到其所需求的資源,解決使用者在檢索數位學習資源時的困難。本篇論文首先設計一套檢索規則與介面,用來記錄資源的使用情況;接著利用社群理論,將較熱門、較受歡迎、較受好評的資源,視為較有價值的資源,檢索結果的排序將依照此資源價值的高低排序;最後,利用測量個人的使用情境與週期,將較貼近使用者使用情境的資源重新給予評分,使得最終檢索結果重新排序,達到個人化推薦的機制,使得使用者能更快速的找到其所需求的資源。
    With the quick development of Internet, Digital learning resources in the network have continued dramatic increase. Although nowadays users can use increasingly abundant resources and powerful search engine to find resources conveniently, but they often need to filter out the huge searched results before finding the real need. Therefore, the main purpose of this paper is to develop a search service that enables users to quickly find the resources they need and to improve difficulties to retrieve e-learning resource.
    In this paper, we first design retrieve rules and develop an interface to record the Service condition. Then, based on the social theory, we consider the more popular and more critically acclaimed resources are more valuable ones and sort the results in accordance with the level of the value. Finally, by measuring the individual situations and cycle of the user, we re-evaluate and re-sequence the resources so that the final re-ordered result will optimally meet the requirement of personalized recommendation and make users more quickly find the resources they need.
    顯示於類別:[資訊工程學系暨研究所] 學位論文

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