臺灣博碩士論文加值系統

網路與電子商務的急遽成長帶來了資訊的超載，顧客得到了網路購買在時間空間上的方便，卻也同時得到了網路購買在商品資訊方面過多而無所適從的狀況；在企業市場中，少量的消費者帶來龐大的購買力，金字塔頂端的顧客對於企業的營運佔有舉足輕重的地位，為了要使顧客有備受重視的感覺，不僅要提供優質的產品與服務，更要能夠深入瞭解顧客，提供個人化的行銷策略，藉以提昇顧客忠誠度，加強關係行銷；因此就出現了許多個人化推薦系統與方法，包括了傳統的協同過濾法、RFM導向之協同過濾法，以及RFM導向之關聯法則探勘法，而其中，協同過濾法為一同時具備效果與效率，並且獲得廣泛使用之推薦方法，但其也擁有兩大缺點與限制，即為稀疏與規模延伸性的問題；本研究提出一個混合式的個人化推薦方法，基於協同過濾法的優點，再加入調整後之產品分類學，將爆炸的資訊和產品化繁為簡，透過真實資料集的實驗以及與關聯法則探勘法和傳統RFM導向之協同過濾法之比較，本研究提供之方法確實擁有更好的推薦品質。

Rapid growth of Internet and the e-commerce environment caused information overload. Customers receive the benefit of e-commerce but they also undertake the shortcoming it has – the explosion of information. Although there are only few customers in business market, they bring enormous purchase ability. Customers, who are more valuable to the enterprises, are very important. In order to attach importance to the customers, retailers need to not only provide the excellent quality of products and services but also know the customers well. It’s important to provide personal marketing strategies and to improve customers’ loyalty to the enterprise. To enhance the relationship marketing, a variety of recommendation systems and methods have been developed, such as typical CF, RFM-based CF and RFM-based association rule mining method. Collaborative filtering is one of the most successful methods. It not only very effective and efficient but also been widely used in various applications. Its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This paper proposes a hybrid recommendation method that based on CF method and adjusted product taxonomy. This method tends to make the information and products less complicated. Several experiments on real e-commerce datasets show that the proposed methodology dose provide better quality of recommendations and better performance than association rule mining method and typical RFM-based CF methodology have.

目錄
摘要 i
ABSTRACT ii
致謝 iii
目錄 v
表目錄 viii
圖目錄 vii
一、研究動機與目的 1
二、文獻探討 3
2.1 企業市場與顧客回應性 3
2.1.1 企業市場 3
2.1.2 顧客回應性 3
2.2 顧客終身價值分析與區隔化 4
2.2.1 顧客終身價值分析 5
2.2.2顧客區隔化 7
2.3 資料探勘與推薦系統 7
2.3.1 資料探勘 7
2.3.1.1群集分析 8
2.3.2 推薦系統 10
2.3.2.1關聯法則探勘 12
2.3.2.2協同過濾法 13
2.4 產品分類學 14
三、研究方法 16
3.1 研究流程 16
3.2 使用工具 17
3.3 RFM分析 18
3.4 顧客終身價值分群 20
3.5 產品分類調整 22
3.6 協同過濾分析 30
3.7 個案介紹 – N公司 31
3.8 資料處理 33
四、驗證與評估 34
4.1 資料集 34
4.2 實驗設定 34
4.3 實驗結果 35
5.3.1 AT-CF法與關聯法則探勘法之比較 38
5.3.2 AT-CF法與RFM-CF之比較 39
五、結論、研究限制與未來研究 42
5.1 結論 42
5.2 研究限制 42
5.3 未來研究 43
六、參考文獻 44
附錄 49

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