隨著網路科技的快速崛起，使得資訊發展所帶來的便利在生活上無所不再，而資料成長速度更是一日千里，若想在浩瀚的資料海中，尋找實際有用的資訊則相對少量，正是企業與學者主要關注的議題。為了增加企業的利益，業者必須充分蒐集與客戶相關的資訊，來經營與客戶之間的關係，以資料探勘技術來了解客戶喜好的變化，甚至利用機器學習來發展與客戶互動的智慧型商業平台，而近年來使用推薦技術應用在客戶與產品之間的行銷與決策。以電子商務而言，用於推薦分析主要是根據客戶的評價資料與交易資料，在推薦領域上，評價資料就是客戶直接給產品評價的顯性評比，而交易資料則像是客戶在網路上購買或瀏覽商品時所遺留下來的瀏覽率與購買頻率，稱為隱性評比。而本論文希望可以提出一個協同過濾的推薦架構，能應用在評價資料與交易資料上的推薦，透過客戶資料在推薦架構的應用能用在教學研究，以簡單易學的架構與流程，透過實例運算與實際資料集的測試讓初學者能快速產生連結；除此之外，本架構還可用在資料測試並讓用戶可將推薦結果與其他推薦系統作比較，讓用戶評估並選擇最佳的推薦效果。

Due to the rapid development of Internet, it brings humans not only more convenient in life but also makes data growth up rapidly. People face with the amount of information available and expect to use it efficiently. In order to increase the benefit, businessman must fully collect information related to customer and manage customer relationship. Recommender system has been applied for cross-selling or increasing profit between customers and products in recent years. It has been become popular for making useful recommendation to online e-commerce sites. In this paper, we mainly use the rating data or transaction data applied in recommender system. The rating data from item rated directly by customers is called explicit rating. Also, the transaction data, such as web browsing clicks, purchased records and so on, is called implicit rating. We expect to provide a framework of recommender system that employs collaborative filtering to derive recommendations. The framework can be used for teaching and research. Moreover, the process can offer a practical computation instances and dataset for freshman to learn and map easily. In this framework users can utilize data for measuring, or find out proper recommendation results through comparing with other techniques.

第1章 緒論	1
1.1 研究動機與目的	1
1.2 研究架構	4
第2章 文獻探討	5
2.1 以內容為基礎的推薦系統	6
2.2 協同過濾推薦系統	15
2.3 混合式推薦系統	29
2.4 比較以內容為導向的推薦與協同過濾推薦	33
第3章 研究方法	37
3.1 基本架構	38
第4章 研究探討	46
4.1 討論顯性評價的使用	47
4.1.1 預測與精確度評估	48
4.1.2 以客戶為導向的協同過濾	50
4.1.3 以產品為導向的協同過濾	54
4.2 交易資料的討論	58
4.2.1 輸入格式	59
4.2.2 相似度計算與推薦方式	62
第5章 結論	64
參考文獻	66
附錄  英文論文	71
 
圖目錄
Figure 1 協同過濾的處理步驟	24
Figure 2 User-based推薦流程	50
Figure 3 User-based預測評價的輸入	51
Figure 4 User-based的主要預測處理流程	52
Figure 5 User-based的預測結果與整體精確度	53
Figure 6 Item-based推薦流程	54
Figure 7 Item-based預測評價的輸入	55
Figure 8 Item-based的主要預測處理流程	56
Figure 9 Item-based的預測結果與整體精確度	56
 
表目錄
Table 2-1 以一組屬性表達餐廳型態	7
Table 2-2 結構化與非結構化資訊之間的差異	9
Table 2-3 CB常見缺點	13
Table 2-4 客戶對產品的評價矩陣	16
Table 2-5 常用評價方式	17
Table 2-6 顯性與隱性評價機制	18
Table 2-7 User對Item的評價矩陣	21
Table 2-8 混合式策略的主要模式	31
Table 2-9 CB與CF之比較	35
Table 3-1  User-based與Item-based相似度比較表	44

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