臺灣博碩士論文加值系統

本篇論文以搜尋論文為例，希望可以解決使用者在搜尋論文時，對於以往傳統的關鍵詞查詢之後瀏覽數目龐大的繁雜檢索。因為瀏覽的方式雖可以網羅到和關鍵詞查詢相關的所有資訊，但卻會造成資訊過載的問題，甚至許多的內容對於使用者真實想要檢索的資訊有相當大的出入，以致資訊檢索的困難。因此本篇論文使用了機器學習的技術，包括了案例式推論、叢集分類法等，希望能夠對於使用者在檢索論文的時候，可以有效的縮短檢索資訊的時間，且提高檢索資訊的正確度。此外，使用者搜尋資訊的同時也能累積經驗，間接的協助其他的使用者檢索資訊，並因為個人的興趣及領域不同，因此又結合了案例式推論的方法，希望不同使用者的分類角度能夠提供給新使用者參考檢索的依據，提高資訊檢索的正確度及效率。

本研究提供了一個論文分類的系統，藉以蒐集使用者的分類行為，最後利用了兩種分類方法：案例式推論協同推薦與關鍵詞協同推薦；前者使用的是行為最相似的使用者資訊作為推薦，然而普遍可以得到較高的準確率，但必須承擔較大的風險，後者採用輸入相同關鍵詞的使用者一同推薦查詢者，相對的承擔較低的風險，而可以得到比較平穩的正確率。

關鍵詞: 協同推薦、叢集法、案例式推論。

This paper tries to apply the clustering approach and collaborative recommendation for using the present experience to make the new users in indexing or searching papers fast and correctly. Besides the user’s experience is very important for the recommendation system, because the past cases could help the new user more quickly to index the paper. We suggest the CBR(Case-Based Reasoning) method for the system remaining the past indexed cases to help new user to index the paper based on the cases which had grouped by the past uses straightly.

This research applied a system for the user to training their searching behavior and record the information for recommendation of the new user to index the paper. We used the information and applied two kinds of the collaborative recommendation. One of the methods is the keyword collaborative recommendation and the other is the CBR collaborative recommendation. We find out that CBR approach is better than keyword’s since it tries to find out the most similar person to make the recommendation. But it also has the high risk opposite to the keyword approach. Since the keyword approach use the users who have the same keyword would suggest the new users together, it could have the stable precision and the lower risk to get the worse recommendation.

Key words: Collaborative recommendation, Clustering, Case-based Reasoning.

摘要………………..……………………………………………………………………... 1
Abstract……………………………………………………………………………………2
目錄………………………………………………………………………………………..3
圖目錄……………………………………………………………………………………..4
表目錄……………………………………………………………………………………..5
第壹章 緒論………………………………………………………………………………6
第一節 研究動機……………………………………………………………………6
第二節 協同分類……………………………………………………………………7
第三節 研究目的…………………………………………………………………..10
第四節 研究限制…………………………………………………………………..11
第貳章 文獻探討………………………………………………………………………..13
第一節 資訊檢索…………………………………………………………………..13
第二節 資訊過濾…………………………………………………………………..15
第三節 個人化……………………………………………………………………..17
第四節 協同過濾…………………………………………………………………..22
第五節 叢集分析法………………………………………………………………..26
第六節 案例式推論………………………………………………………………..29
第參章 協同推薦方法…………………………………………………………………..31
第一節 輸入資料定義……………………………………………………………..31
第二節 叢集分類步驟……………………………………………………………..33
第三節 瀏覽與索引的方法………………………………………………………..40
第四節 協同推薦…………………………………………………………………..42
第肆章 系統實作與實驗結果評估……………………………………………………..49
第一節 協同過濾評估方法………………………………………………………..49
第二節 本研究驗證方法與實驗設計……………………………………………..52
第三節 系統開發工具……………………………………………………………..53
第四節 實驗設計…………………………………………………………………..53
第五節 實驗結果…………………………………………………………………..54
第六節 實驗分析…………………………………………………………………..56
第伍章 結論與未來研究方向與建議…………………………………………………..60
第一節 結論………………………………………………………………………..60
第二節 未來的研究方向與建議…………………………………………………..62
參考文獻……………………………………………………………..…………………..64

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