現今21世紀的大環境中，因應電腦的普及化與社會經濟的復甦，對於網路購物已漸趨謂為風潮，在傳統的行銷系統中，幾乎每隔10-20年，就會有一套新的行銷系統出現，成為將產品銷售給終端消費者的新方法，所以在1990年代開始，網路與電子商務的盛行，至今因科技的進步、電腦的普及與消費者訴求購物的便利性，創造出所謂的「宅經濟」。
因此，網路購物市場越來越蓬勃發展，而許許多多的消費者所傾向的是價格上低廉、交易機制的便利與安全性，因此，各網路購物平台的消費者忠誠度並不高，為獲得更廣大的消費者上網購買商品，與幫助企業的網站瀏覽量大增，提高各網路平台的消費者忠誠度，本研究將設計一個個人化的推薦機制，提供消費者更精緻的購物環境與服務。
本研究結合了約略集與資料採礦中的關聯法則，著重於具有處理不確定性資料能力的規則產生，以助於行銷決策者可以準確的區隔市場，並發展以約略集為基礎之關聯演算法，加上層級分析法(AHP)之相對比重概念，建立內外部推薦機制，將適當的產品與平台通路推薦給消費者，端看是否能改變其消費行為。

Today in the 21st century environment, due to the popularity of computers and the social economic recovery, online shopping is getting that trend. In the traditional marketing system, almost every 10-20 years, will have new marketing system, to become a new method of selling product to the end of consumer. 
So, in the beginning of the 1990s, the prevalence of the Internet and e-commerce, so far, due to advances in technology, the popularity of computers and consumer demands of shopping convenience, and create the called”home economic”.
Therefore, the online shopping market is increasingly prosperous development, while many consumers tend to price low, the convenience and security of the trading mechanism, therefore, the online shopping platform is not high consumer loyalty, in order to attract a wider consumer to purchase goods, and help enterprises significant increase in the website traffic and improve the network platform of consumer loyalty, this study will design a personalized recommendation mechanism, providing consumers with more refined shopping environment and service.
In this study, a combination of rough sets and data mining of association rules, focus on the rules of dealing with uncertainty information generated to help the marketing decision-makers can accurately segment the market and the development of the rough-set based association rule algorithm, together with the relative weight of the concept of the Analytic Hierarchy Process (AHP), the establishment of external and referral mechanism will be the appropriate product and platform path recommended to consumers, the end to see if it can change their consumption behavior.

目錄
致謝 I
中文摘要II
英文摘要 III
目錄IV
表目錄 VII
圖目錄X
第一章緒論
1.1研究背景與動機1
1.2研究問題與目的2
1.3研究方法與流程4
第二章文獻探討
2.1約略集理論5
2.1.1約略集理論的定義6
2.1.2約略集理論運用的領域8
2.1.3約略集與本研究的關係10
2.2關聯法則10
2.2.1關聯法則的定義10
2.2.2關聯法則運用的領域12
2.2.3關聯法則與本研究的關係13
2.3 推薦機制13
2.3.1推薦機制的定義14
2.3.2推薦機制的種類15
2.3.2-1內部推薦(商品類別推薦)15
2.3.2-2外部推薦(通路推薦)17
2.3.3推薦機制與本研究的關係18
2.4改變行為18
2.4.1改變行為的定義19
2.4.2改變行為運用的領域19
2.4.3改變行為與本研究的關係20
2.5資料採礦21
2.5.1資料採礦與約略集理論23
2.5.2資料採礦與推薦機制23
2.5.3資料採礦與本研究的關係24
第三章研究方法
3.1約略集理論與關聯法則的結合25
3.2約略集理論與關聯法則解決問題的資料型態與衡量尺度25
3.3演算法概念生成26
3.4順序尺度(Ordinal Scale)資料30
3.5推薦機制設立31
3.5.1通路推薦機制演算法之流程47
3.5.2商品類別推薦機制演算法之流程67
第四章實證分析
4.1順序尺度於網路購物平台之應用72
4.2 Yahoo與PChome強勢通路之範例72
4.3 PChome與MoMo強勢與弱勢通路對比之範例80
4.4 順序尺度於網路購物平台─商品類別之應用84
4.5 Yahoo平台強勢通路下之商品類別範例84
4.6 MoMo平台弱勢通路下之商品類別範例91
第五章演算法驗證
5.1改變行為之問卷設計99
5.2改變行為之問卷發放99
5.3相同顧客輪廓下其Yahoo與MoMo商品類別推薦之效果101
5.4相同顧客輪廓下其Yahoo與MoMo平台推薦之效果104
5.5改變行為驗證之結論106
5.6推薦機制與改變行為之比較106
5.6.1推薦平台：Yahoo與MoMo→由Yahoo改變至MoMo107
5.6.2推薦商品類別：消費電子、精品手錶
美妝保養與團購美食→改變成套裝組合107
5.6.3推薦平台與商品類別的合作機制108
第六章結論與管理意涵
6.1研究結論與建議112
6.2管理意涵113
6.3平台業者與商品業者之競合策略116
6.4研究限制與後續研究120
6.4.1探討約略集理論處理開放性重複排序值120
6.4.2從智慧型手機探討網路購物與建立推薦機制120
6.4.3建立社群購物模式與社群推薦機制120
6.4.4從一對一行銷結合網路購物新趨勢121
參考文獻
一、中文參考資料122
二、英文參考文獻124
三、網路資料130
附錄一、前測問卷131
附錄二、正式問卷146
附錄三、驗證問卷159
表目錄
表2-1約略集理論運用的領域之研究9
表2-2關聯法則運用的領域之研究12
表2-3-1資料庫期刊資料內部推薦機制運用的領域之研究15
表2-3-2資料庫期刊資料外部推薦機制運用的領域之研究17
表2-3-3中文學位論文外部推薦機制運用的領域之研究18
表2-4資料庫期刊資料消費者改變行為運用的領域之研究19
表2-5資料庫期刊資料之資料採礦運用的領域之研究22
表2-5-1資料庫期刊資料之資料採礦與約略集理論運用的
領域之研究23
表2-5-2資料庫期刊資料之資料採礦與推薦機制運用的領域之研究24
表3-1習慣透過「網路購物平台」的排序資料表32
表3-2通路順序尺度資訊系統表33
表3-3順序性資料的核心屬性值35
表3-4平台組合的原始組合36
表3-5相對重要性尺度比重衡量表36
表3-6簡化後之二十八種組合37
表3-7平台組合的資料集合38
表3-8消費者X1平台組合的資料集合39
表3-9消費者X2平台組合的資料集合39
表3-10通路決策資料表41
表3-11不可辨識關係下的屬性值43
表3-12習慣透過「PChome網路購物平台」的商品類別排序資料表53
表3-13 PChome商品類別排序資料系統表55
表3-14順序性資料的核心屬性值57
表3-15相對重要性尺度比重衡量表58
表3-16商品類別的比例資料集合59
表3-17 PChome商品類別之決策資料表61
表3-18不可辨識關係下的屬性值63
表4-1網路購物平台之資訊系統表72
表4-2-1網路購物平台偏好排序之核心屬性集合73
表4-2-2網路購物平台偏好排序之核心屬性集合74
表4-2-3網路購物平台偏好排序之核心屬性集合75
表4-3相對重要性尺度比重衡量表76
方程式4-4 76
表4-5條件屬性資訊表77
表4-6 Yahoo對PChome網路購物平台之決策資料表78
表4-7網路購物平台偏好的約略關聯法則集合79
方程式4-8 80
表4-9 PChome對MoMo網路購物平台之決策資料表81
表4-10網路購物平台偏好的約略關聯法則集合82
表4-11{Yahoo,PChome}與{PChome,MoMo}偏好的約略關聯法則集合82
表4-12 Yahoo網路購物平台之商品類別資訊系統表84
表4-13-1 Yahoo商品類別偏好排序之核心屬性集合85
表4-13-2 Yahoo商品類別偏好排序之核心屬性集合85
表4-13-3 Yahoo商品類別偏好排序之核心屬性集合86
表4-13-4 Yahoo商品類別偏好排序之核心屬性集合86
表4-13-5 Yahoo商品類別偏好排序之核心屬性集合86
表4-14相對重要性尺度比重衡量表88
方程式4-15 88
表4-16 條件屬性資訊表89
表4-17 消費電子與手錶商品類別之決策資料表90
表4-18消費電子與手錶偏好的約略關聯法則集合91
表4-19 MoMo網路購物平台之商品類別資訊系統表92
表4-20-1 MoMo商品類別偏好排序之核心屬性集合92
表4-20-2 MoMo商品類別偏好排序之核心屬性集合93
表4-20-3 MoMo 商品類別偏好排序之核心屬性集合93
表4-20-4 MoMo商品類別偏好排序之核心屬性集合93
表4-20-5 MoMo商品類別偏好排序之核心屬性集合94
方程式4-21 94
表4-22美妝保養與團購美食商品類別之決策資料表95
表4-23美妝保養與團購美食偏好的約略關聯法則集合96
表4-24{消費電子,精品手錶}與{美妝保養,團購美食}偏好的約略關聯法則集合96
表5-1基本統計表99
表5-2經過挑選之61人基本統計表100
表5-3最常購物之網路購物平台統計表102
表5-4商品類別之推薦統計表103
表5-5核心產品混合搭售之統計表103
表5-6網路購物平台之推薦統計表105
表5-7推薦機制與改變行為比較表108
圖目錄
圖1-1研究流程圖 4
圖2-1文獻探討架構圖5
圖2-2近似示意圖8
圖2-3資訊管理的金字塔21
圖3-1 Apriori演算法產生之候選項目集合與高頻項目集合之流程27
圖3-2演算法的概念28
圖3-3推薦演算法的規則產生29
圖5-1推薦機制與改變行為之比較雷達圖107
圖5-2合作機制架構圖109
圖6-1平台業者與商品業者之競合策略圖116

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三、網路資料
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