近年來，推薦系統(Recommendation system)相關議題吸引了許多專家學者的目光，最主要是因網路的蓬勃發展，造成了傳統消費行為的改變，其中協同過濾是推薦系統中採用最廣泛的推薦技術，藉由其他和你相似的使用者的偏好，去預測你的個人偏好，進而達到個人化的推薦效果。但傳統的協同過濾是將不同用戶的興趣，同等考慮，因為現實生活中用戶的偏好是會經常改變的，這就使得在某一段時間的偏好改變對於整個項目中，會顯得並不突出，因此本研究提出一個基於考慮順序效應之協同過濾推薦方法，所以在考量用戶間相似度的時候，同時考慮偏好的順序性。研究結果顯示，考慮偏好順序性的協同過濾推薦方法，可以提高推薦系統預測的正確性。

In recent years, the recommendation system has attracted the attention of many experts and scholars, mainly because of the vigorous development of the Internet, which has caused the change of traditional consumption behavior. The collaborative filtering is the most widely used recommendation technology in the recommendation system , By other similar users and your preferences, to predict your personal preferences, and then achieve the personalized recommendation effect. But the traditional collaborative filtering is the interest of different users, the same considerations, because the real life of the user's preferences will often change, which makes a certain period of time to change the preferences for the entire project, will appear not prominent, so In this study, we propose a collaborative filtering recommendation method based on the sequential effect. Therefore, considering the similarity between users, we consider the order of preference. The results show that the recommended method of cooperative filtering is considered to improve the correctness of the proposed system.

目錄
中文摘要 II
Abstract III
目錄 V
圖目錄 VII
表目錄 VIII
第一章 緒論 1
1.1 引言 1
1.2 研究動機與目的 3
1.3 論文架構 4
第二章 文獻探討 5
2.1 推薦系統 5
2.2 協同過濾 6
2.2.1使用者為基礎之協同過濾 7
2.2.2 項目為基礎之協同過濾 7
2.2.3 序列為基礎之協同過濾 8
2.2.4 混合方法 9
第三章 研究假設 11
3.1 問題描述 11
3.2 User Sequence-Based CF(USCF)架構 12
第四章 系統架構與方法 14
4.1 項目分群 15
4.1.1 H聚類 16
4.2 序列推導 19
4.2.1 定義使用者的評分序列 19
4.3 萃取出喜歡與不喜歡序列 19
4.3.1 喜歡序列定義 20
4.3.2 不喜歡序列定義 21
4.4 相似度計算 21
4.4.1 n元語法 21
4.4.2 傑卡德系數	23
4.5 四種類型方法	23
4.5.1 喜歡序列相似度計算 24
4.5.2 不喜歡序列相似度計算 24
4.5.3 混合型-USCF相似度計算 25
4.5.4 綜合型-USCF相似度計算 25
4.6 推薦預測 26
第五章 實驗與結果 27
5.1 資料蒐集與整理 27
5.2 評估指標 27
5.3 實驗結果與討論 28
第六章 結論 31
參考文獻	32
附錄-英文論文 35

圖目錄 
圖1 User-Based CF與Item-Based CF範例 2
圖2 序列基礎示意圖 9
圖3 USBF架構流程圖 14
圖4 傳統協同過濾無法推薦的範例 16
圖5 H分群在社群發現的範例	18
圖6 不同相似度方法的MAE比較 29
圖7 α值對Hybrid-USCF MAE的影響 30
圖8 β值對Mixed-USCF MAE的影響 30

表目錄
表1 使用者-項目評分序列矩陣 19
表2 使用者-項目評分序列範例 20
表3 喜歡序列範例	20
表4 不喜歡序列範例 21
表5 推薦喜好排序範例 24
表6 預測推薦評分範例 26
表7 MovieLens的特點 27
表8 不同相似度方法的MAE比較 29

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