隨著網路的普及化與「宅經濟」的促使之下，網路線上拍賣系統的業績也不斷攀升。以eBay為例，2007年的網拍業務收入為59.7億美元，而在2010年該預估可能將達到88億美元至91億美元，顯示線上拍賣的蓬勃發展。然而，拍賣網站會員人數動輒百萬計，如何協助使用者在下標前挑選合適的賣家便成為重要課題。目前，網拍平台大多使用二元名聲系統(binary reputation system)來管理交易者的名聲，雖然簡單易懂，但很難從中獲得完整的名聲參考資訊。 有鑑於此，學者們紛紛提出各種不同的名聲計算方式，以協助使用者挑選合適賣家。
面對上述問題，本論文發展了一套線上拍賣基因式模糊名聲推理方法-GFRep。方法中同時考量『商品分類相似性』、『評價時間』、『交易金額』與『評價者可信度』等四種因子，並以模糊推理來計算綜合名聲值。為強化模糊規則的效力，我們利用基因演算法來建立模糊規則庫，對規則的後鍵部與模糊變數的歸屬函數進行最佳化。為驗證系統的有效性，我們蒐集eBay網站上實際的交易資料進行實驗，並與三種不同的方法進行比較。實驗結果顯示GFRep在協助買方挑選合適賣家時，能提供比其餘三種方法更合適的建議。

Because of the universal usage of internet, a name "Otaku Economy" appears. An online auction system's sales increases continual. Take the leader of this online auction systems-eBay for instance. The revenues of auction sales are 5.97 billion dollars in 2007, and the estimation revenues of eBay will reach from 8.8 billion dollars to 9.1 billion dollars. The above-mentioned facts indicate that those users who use the online auction system to trade with others are increasing. The most important issue are how to choose an appropriate auctioneer and an auction website's members are tens of thousands that cause the difficulties of management. The auction websites use binary reputation management now to manage the auctioneer's reputation, but still can't offer entire reputation information. Because of the drawbacks of binary reputation, many scholars announce different calculation of reputation to help users choosing the right auctioneer.
  In order to help users solving above-mentioned problems, this thesis develop a suit of Genetic Fuzzy Reputation Inference Method- GFRep for On-Line Auctions .To offer more perfect calculation methods, this research think about three factors including "deal-time" ,"deal-amount" and "the credit of dependability". We calculate total reputation value by fuzzy inference. To strengthen the efficiency of fuzzy rules, We use genetic algorithm to create fuzzy rule base optimizing the rule's consequent and fuzzy variable's membership function. To validate the system's effectiveness, we gather the transaction information from eBay. The result reveals that GFRep is more reliable than other three methods.

目錄
中文摘要	I
英文摘要	II
目錄		III
圖目錄		V
表目錄		VII
第1章	前言	1
第2章	相關技術與背景知識簡介	4
2.1	線上拍賣現況	4
2.2	模糊理論（FUZZY THEORY）	5
2.3	基因演算法（GENETIC ALGORITHMS）	8
第3章	GFREP-基因式模糊名聲推理方法	13
3.1	名聲值之調整因子	14
3.1.1	交易領域	14
3.1.2	交易金額屬性	15
3.1.3	時間屬性	16
3.1.4	給評者的可信度	17
3.2	使用模糊推理調整二元名聲值	18
3.2.1	模糊變數與其歸屬函數	18
3.2.2	模糊規則資料庫（Fuzzy Rule Base）	22
3.2.3	模糊推論之範例	25
3.3	利用基因演算法最佳化模糊推理	26
第4章	實驗結果與討論	31
4.1	實驗架構說明	31
4.2	實驗環境	33
4.3	實驗結果	34
4.4	討論	37
第5章	結論與未來發展	39
參考文獻	40
附錄A	各種不同DDSR與BINRANGE實驗結果	43

圖目錄
圖 2 1模糊理論流程	6
圖 2 2歸屬函數模型	7
圖 2 3歸屬函數之範例	7
圖 2 4歸屬函數之範例	8
圖 2 5基因演算法流程	9
圖 2 6 單點交配過程	10
圖 2 7 雙點交配過程	11
圖 2 8 字罩交配過程	11
圖 2 9 突變之範例	12
圖 3 1問題陳述之圖例	13
圖 3 2 本研究提出方法之流程圖	14
圖 3 3 交易金額所對應到的交易金錢屬性值	16
圖 3 4  系統模糊設定檔中模糊變數	18
圖 3 5此系統對於模糊模型之命名	19
圖 3 6 設定檔之歸屬函數設定	22
圖 3 7 系統預設之125條模糊規則	24
圖 3 8  模糊推理與解模糊化之範例	26
圖 3 9  遺傳演算法初始族群	27
圖 3 10  適應函數值之演算法表示	30
圖 4 1  EBAY中DETAILED SELLER RATING(DSR)畫面之範例	32
圖 4 2此研究系統模糊設定檔	33

表目錄
表 3 1   物種交配與適應函數值之範例	29
表 4 1  比較二種不同名聲計算方式優劣之可能結果	32
表 4 2 以NIKON分類下之資料進行基因式模糊推理實驗	34
表 4 3 以EBAY網站NIKON分類下所有帳號配對做為測試資料所得之實驗結果	35
表 4 4 以EBAY網站IBM/LENOVO分類下所有帳號配對做為測試資料所得之實驗結果	37
表 6 1 NIKON BINRANGE = 50	43
表 6 2 NIKON BINRANGE = 100	44
表 6 3 NIKON BINRANGE = 150	45
表 6 4 NIKON BINRANGE = 200	46
表 6 5 IBM BINRANGE = 50	47
表 6 6 IBM BINRANGE = 100	48
表 6 7 IBM BINRANGE = 150	49
表 6 8 IBM BINRANGE = 200	50

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