根據中時電子報的報導指出，公元二○○一年全球中文上網人口將達到三千五百萬人，這顯示網際網路上聚集了大量的閱聽人，其地位已經趕上報紙和廣播電視，成為重要的傳播媒體，因此，發展電子報已經成為媒體必然的趨勢。
目前國內外有許多新聞的網站，但是由於人類處理資訊能力有限，使得閱聽人在有限時間內無法接觸到自己真正有興趣的新聞，於是有不少研究致力於新聞的過濾與推薦。雖然國外有不少資訊過濾系統的相關研究，但是仍缺乏一套完整的方法來有效的建構推薦機制。有鑑於此，本研究著眼於電子報新聞的過濾與推薦，主要目的有二：(1)提出一套個人化的電子報新聞過濾推薦機制。(2)開發一套個人化的電子報新聞推薦之雛型系統，並進行實證研究來驗證推薦機制的績效。
實證研究的目的在於驗證「推薦機制」對於客觀的「使用績效」與主觀的「滿意度」之影響。實證結果發現，不論在使用績效或者滿意度上，具有推薦機制的系統之表現都優於一般系統，顯示推薦機制確實是有效的。另外，本研究也比較了兩種不同推薦機制(自評推薦vs系統推薦)的效果，其中自評推薦必須要求閱聽人界定其新聞興趣，而系統推薦則是根據閱聽記錄自動推導出興趣，作為推薦的依據。研究結果顯示，不論在使用績效或者滿意度上，系統推薦與自評推薦的效果都沒有顯著差異，因此，利用系統推薦法可以不需要閱聽人的回饋，有效地找出閱讀興趣並推薦個人化的新聞。
本研究的主要貢獻在於提出一套個人化的電子報推薦機制之建構方法。此套電子報新聞推薦機制的特色，在於結合新聞內容分析與使用者輪廓分析以達到個人化的推薦，並且具有學習能力，利用本推薦機制，閱聽人不須費心評估自己的興趣，就可以輕鬆獲取個人化的新聞。本研究的成果不僅可以讓學術界更進一步了解個人化推薦機制的建構方式以及它在新聞閱讀上的影響，同時也可以作為實務界建置電子報新聞推薦系統的參考。

Electronic Newspapers has become one sort of important communication medium with the rapid growth of audiences on Internet. In Taiwan, the majority of famous news media have their own Electronic Newspapers, which provide a large amount of online news. Though audiences can receive a mass of news, it is difficult for them to find the news in which they are really interested within limited time. Therefore, this current study focused on the research of the mechanism for filtering and recommending news based on the Use and Gratification Theory. The purposes were to develop a personalized news recommendation method for Electronic Newspapers and to examine the effect of the recommendation methods.
The results of the empirical study showed that the system with recommendation mechanism was superior to the one with none in both usage performance and overall satisfaction. In this study, there were two recommendation mechanisms. One of them is time-based mechanism which refers to derive the interest automatically by analyzing how much time the audiences spent on reading news, the other is feedback-based mechanism which refers to derive the interest by asking audiences explicitly. It was found that there were no significant differences between the two mechanisms in either usage performance or overall satisfaction. Therefore, the time-based mechanism is helpful for audiences in finding personal news.

目錄
第一章 緒論
第一節 研究背景 1
第二節 研究動機 5
第三節 研究目的 7
第四節 研究方法 8
第五節 論文之結構 10

第二章 文獻探討
第一節 使用與滿足理論 11
第二節 資訊處理相關理論 15
第三節 過濾推薦系統的相關研究 17
第四節 閱聽人興趣推導的相關研究 21
第五節 績效衡量的研究 24

第三章 推薦機制
第一節 新聞過濾推薦機制之架構 26
第二節 新聞結構分析 27
第三節 閱聽人輪廓分析 32
第四節 評分推薦機制 37
第五節 學習機制 40

第四章 雛型系統
第一節 雛型系統架構 42
第二節 使用軟體介紹 44
第三節 使用流程 48
第四節 系統彈性與使用限制 57

第五章 實證研究
第一節 研究模式與變數 59
第二節 變數的衡量 60
第三節 研究假說 66
第四節 實驗設計 67
第五節 實驗進行流程 70


第六章 結果分析與討論
第一節 效度與信度 72
第二節 基本敘述統計分析 74
第三節 自評推薦機制的效果 76
第四節 系統推薦機制的效果 80
第五節 不同推薦機制的效果比較 85
第六節 個人特質變數對效果的影響 89
第七節 系統參數設定之探討 92
第八節 結論 93

第七章 結論
第一節 研究結果與貢獻 94
第二節 研究限制 96
第三節 後續研究 97


參考文獻 98

附錄一：閱聽人行為記錄範例 103
附錄二：問卷 106

簡歷 112

參考文獻
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