推薦系統普遍應用在電子商務、知識管理、文件管理等領域，為了改善傳統的推薦系統的推薦品質，本研究將「時間」與「利潤」兩個因素加入推薦系統的運作，提出「以顧客動態購買興趣樣式為基礎並考慮產品利潤」之協同推薦系統，主要目的為偵測出客戶隨著時間變異而改變的購買行為，進而在正確的時間推薦正確的商品給顧客，並站在廠商的立場上加上利潤考量，企圖在不失去推薦準確度的狀況下，產生更高的利潤，造成顧客與企業雙贏的局面。
本研究提出RFP AprioriAll演算法，將傳統AprioriAll序列樣式探勘演算法加入「時間」與「利潤」兩個因素，用來產生更具有意義的顧客購買行為之序列樣式；並且搭配考量利潤的協同推薦系統架構，進一步提高銷售的利潤。
本研究所設計的推薦系統包含五個主要步驟：(1)建立顧客的動態興趣輪廓檔、(2)使用分群演算法將顧客分群、(3)使用RFP AprioriAll序列樣式演算法來預測顧客未來感興趣的產品分類、(4)根據序列樣式支持度與利潤計算產品類別分數和(5)依據前幾名產品分類下的產品熱門程度與利潤計算分數並做出最後個人化的產品推薦。
本研究使用IBM Almaden Research Center提供的Assoc.gen模擬程式產生模擬的顧客交易行為紀錄和真實世界中的超市資料庫(FoodMart)作為實驗的資料來源，進行預測目標顧客未來的購買行為，預測結果經無母數統計檢定得到以下結論：在顧客的購買行為會隨著時間變異而改變的條件下，本研究提出之推薦系統架構能在推薦準確度沒有顯著變化的情況下，明顯增加銷售的利潤。

This study propose a sequential pattern based collaborative recommender system, which adds “time” and “profit” factors to the operation of the recommender system to improve the quality and profit performances of the collaborative recommender systems. The purposes of this study are to detect the customer time-variant buying behavior, and then to recommend the right products and high profit products to the right customers in the right time.
To recommend the right products and high profit products, this study proposed a RFP Apriori algorithm that considered the last purchasing time (recency) and the product profit, to discover the RFP-Patterns, and combine to a profitable recommender system.
The framework of system includes five steps: (1) create dynamic customer profile, (2) find the k nearest neighbors using a clustering algorithm, (3) predict product category using the RFP AprioriAll sequential pattern algorithm, (4) compute the product category score and select Top-M product category, and (5) compute the Top-N product item score and recommend Top-I product item to the target customers.
This study used a food mart and simulated database to examine the performance of the proposed approach and compared it with the Top-N recommendation. The recommendation accuracy is similar to the Top-N recommendation, but the profit of the proposed system is much better than that of the Top-N recommendation.

摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 ix
壹、緒論 1
一、研究背景與動機 1
二、研究目的 3
三、研究範圍與限制 3
四、研究流程 3
五、論文架構 4
貳、文獻探討 5
一、推薦系統 5
(一) 內容過濾式推薦系統 5
(二) 協同過濾式推薦系統 7
(三) 混合式推薦系統 12
(四) 推薦系統相關研究 13
二、群集分析 14
(一) K-means分群演算法 15
三、RFM分析 16
四、利潤探勘 17
五、序列樣式探勘 18
(一) AprioriAll演算法 18
1. 排序(sort)階段 19
2. 大型項目集(Litemset)階段 19
3. 轉換(transformation)階段 19
4. 序列(sequence)階段 19
5. 最大(maximal)序列階段 19
六、RFM-Apriori演算法 23
参、系統架構 25
一、RFP AprioriAll演算法 25
(一)RFP AprioriAll演算法步驟 26
(二)實例說明RFM AprioriAll演算步驟 28
1. 產生大型1-候選序列並計算其支持度 28
2. 移除支持度與利潤較低的候選序列 28
3. 計算大型2-候選序列並移除支持度低的序列 31
(一)模型建立階段 34
1. 計算產品利潤與產品類別之平均利潤 34
2. 建立顧客的動態興趣輪廓檔(Dynamic Customer Profile) 34
3. 利用分群演算法將動態輪廓檔分為k群 35
4. 針對各群顧客過去一段期間的交易類別產生序列樣式集合 36
(二)產生推薦階段 36
1. 選擇目標顧客 37
2. 建立目標顧客的動態興趣輪廓檔 37
3. 計算目標顧客所屬群集 37
4. 針對目標顧客過去的交易類別計算候選序列樣式集合 37
5. 比對顧客所屬群集之序列樣式與候選樣式預測顧客興趣之類別 38
6. 以類別利潤與顧客興趣為基礎計算並產生TopM推薦類別 38
7. 從推薦類別中以產品利潤與項目熱門程度產生TopN推薦項目 40
8. 從M N個推薦項目中依照產品分數產生TopI推薦項目 41
(三)實例說明本研究提出之推薦系統架構 42
肆、實驗展示 47
一、實驗資料來源 47
(一) 模擬資料集 47
(二) 真實世界資料集 48
二、實驗環境 49
三、實驗設計 49
(一) 本研究提出之系統架構 49
(二) 傳統推薦方法 50
(三) 系統參數說明 50
四、實驗評估指標 52
五、模擬資料集之實驗與結果 53
(一) 系統建構參數說明 53
1. 建立顧客的動態興趣輪廓檔 53
2. 利用分群演算法將動態輪廓檔分為k群 53
3. 針對各群顧客過去一段期間的交易類別產生序列樣式集合 54
4. 以類別利潤與顧客興趣為基礎計算並產生TopM推薦類別 54
5. 從推薦類別中以產品利潤與項目熱門程度產生TopN推薦項目 54
6. 從M N個推薦項目中依照產品分數產生TopI推薦項目 54
(二) 實驗結果分析與討論 55
1. 比較RFPP、Collaboration與Random 55
2. 比較*F**、 RF**與*FPP 57
3. 比較RF*P、RFP*和RFPP 59
4. 無母數檢定 61
六、真實世界資料集之實驗與結果 63
(一) 系統建構參數說明 63
1. 建立顧客的動態興趣輪廓檔 63
2. 利用分群演算法將動態輪廓檔分為k群 63
3. 針對各群顧客過去一段期間的交易類別產生序列樣式集合 64
4. 以類別利潤與顧客興趣為基礎計算並產生TopM推薦類別 64
5. 從推薦類別中以產品利潤與項目熱門程度產生TopN推薦項目 64
6. 從M N個推薦項目中依照產品分數產生TopI推薦項目 64
(二) 實驗結果分析與討論 65
1. 比較RFPP、Collaboration與Random 65
2. 比較*F**、 RF**與*FPP 67
3. 比較RF*P、RFP*和RFPP 69
4. 無母數檢定 71
七、敏感度分析 72
(一)Recency分析 73
(二)Gap分析 75
(三)Weight分析 77
(四)Profit分析 79
(五)推薦數量分析 81
伍、結論 83
一、學理上的貢獻 84
二、應用面的貢獻 84
三、未來研究方向 84
參考文獻 86

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