近年來，網路技術的快速發展讓人們對於網路的需求也越來越高，peer-to-peer(P2P)分散式網路結構因而出現取代傳統主從式網路架構，也完全改變了網路應用環境，許多知名的P2P檔案分享軟體接連出現，也成為目前主要的下載方法，但大量的使用人數亦讓分享系統中的分享檔案數量暴增，使用者無法有效利用這些龐大的檔案海，所以即使擁有極為豐富之資源，使用者無法利用亦是枉然。
本論文為了解決上述之問題，因而提出一個多媒體推薦代理人，它可以幫助使用者處理這些資料海而不讓使用者直接面對，本代理人主要根據使用者過往之下載記錄而主動推薦使用者適合之檔案，並會隨著使用者喜好的改變而改變推薦方向，代理人設計架構結合資料探勘與內容導向推薦作為主軸，並利用現有技術與資源(如RSS)降低建置成本且達最大推薦功效，本論文最後依設計實做出系統進行實驗以驗證系統機效，根據實驗結果顯示受測者對於本論文所提出之系統幾乎都持正面評價。

In the recent years, user demands in Internet applications are getting higher and higher due to the rapid development of communication technologies. The distributed peer-to-peer (P2P) network architecture was thus developed to replace the traditional client-server architecture that cannot provide the required quality of services of many applications. The emergence of a lot of P2P file sharing software has totally changed the way how people access files on the Internet. With this kind of evolution, the amount of files in the P2P sharing system increases dramatically. Therefore, a mechanism that helps users to efficiently utilize the huge amount of shared files is preferred. Otherwise, the value and the practicability of P2P applications decrease.
In this paper, a multimedia recommendation agent in the P2P environment is proposed. Its purpose is to help users to get the desired files more easily and efficiently without spending too much time in searching. The agent recommends the multimedia files that a user may be interested in by pushing file information to the user according to his/her download history. The agent can also adjust the recommendation results if the user has some other preferences. We integrated data mining and push technologies to develop the agent. We also adopted several current technologies and resources, e.g., RSS, to have better results while reducing the cost. We have also built the system and held an experiment to test and verify the performance of the agent system. The result shows that testers almost have positive appraisals toward our system.

中文摘要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖索引 v
表索引 vi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與方法 9
第三節 論文架構 11
第二章 相關研究 13
第一節 Peer-to-Peer分散式網路 13
第二節 代理人 22
第三節 Push主動推播機制 23
第四節 資料探勘 28
第五節 小結 34
第三章 系統架構 35
第一節 使用者偏好之建置 41
第二節 內容導向推薦機制之建置 48
第四章 系統發展 53
第一節 開發環境 53
第二節 系統功能 54
第五章 效益評估 60
第一節 衡量變數 60
第二節 實驗設計 62
第三節 實驗進行流程 63
第四節 資料分析 65
第五節 問題探討 73
第六章 結論與建議 75
第一節 結論 75
第二節 研究限制 76
第三節 未來研究建議 78
參考文獻 80

圖1-1 主從式網路架構圖 2
圖1-2 P2P 網路架構圖 2
圖1-3 代理人的角色 5
圖1-4 研究方法流程 11
圖2-1 混合式P2P 架構圖 16
圖2-2 純粹P2P 架構圖 16
圖3-1 代理人系統元件圖 38
圖3-2 使用者偏好建構流程 42
圖3-3 內容導向推薦機制的建構流程 50
圖4-1 推薦代理人主畫面 54
圖4-2 系統選項選單畫面 55
圖4-3 系統基本設定畫面 56
圖4-4 系統初始化畫面 57
圖4-5 系統文字分析畫面 57
圖4-6 檢視選單畫面 58
圖4-7 已偵測檔案畫面 58
圖4-8 推薦清單畫面 59
圖4-9 推薦清單畫面 59

表5-1 受測者基本資料 65
表5-2 受測者推薦滿意度記錄 67
表5-3 受測者平均滿意度與總推薦次數(a) 68
表5-4 受測者平均滿意度與總推薦次數(b) 68
表5-5 受測者下載推薦記錄 69
表5-6 受測者推薦接受率(a) 69
表5-7 受測者推薦接受率(b) 70
表5-8 受測者則數命中率(a) 70
表5-9 受測者則數命中率(b) 70
表5-10 系統滿意度問項與結果 72
表5-11 受測者系統滿意度問卷填寫記錄 72

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