線上拍賣的蓬勃發展有目共睹，此種透過網路買賣物品的方式，已成為許多人生活的一部分。雖然網路有助於打破交易的時間與空間限制，但其隱匿性也讓交易者承擔更大的風險性與不確定性。因此，爲協助會員挑選、過濾交易對象，許多拍賣網站均提供簡易的二元名聲系統(Binary Reputation System)來管理會員名聲。但此種名聲系統設計過於簡單，除了無法真實反應交易者的實際名聲外，也常常受到有心人的操弄，甚至淪為詐騙者的工具。
有鑑於此，本研究以發展更合理的線上拍賣名聲模型為目標，提出了一套新的多屬性相對名聲計算方法。與前人研究不同之處，我們在計算特定會員的累計評價時，同時考量『領域相似度』、『交易金額』、『時間衰減度』與『評價信任度』等屬性，以得到更合理的名聲值。此外，本研究也提出以相對名聲而非絕對名聲來比較會員信譽，以避免高評價者一直處於絕對優勢的不合理狀況，並降低有心人操弄評價系統的動機。為驗證提出方法之有效性，我們蒐集拍賣網站的實際交易資料進行分類測試，結果顯示本研究提出的多屬性相對名聲模型能確能提供比現行二元名聲系統更精確之服務品質預測。尤其，在挑選名聲近似的交易對象時，更能獲得具參考價值的資訊。

The popularity of on-auction is obvious to all, trading goods on the auction sites has become a part of daily life of a modern citizen. Although the restriction on the trading time and location can be greatly alleviated by the network, however, a more risky and uncertain trading nature is involving in such a anonymous environment. To assist the members in choosing a proper trading partner, the auction sites provide a simple binary reputation mechanism to manage the reputation of members. However, it is proven that the binary reputation system is too simple and is hard to reflect the truly reputation.
Focusing on this problem, a novel multi-attribute relative reputation calculation method is proposed in this paper to help the user selecting a proper trading partner. Different from the previous works, a relative but not a absolute reputation value is used to compare the trusted degree of two different members. Besides, to derive a more accurate trust model, the similarity and the transaction amount of commodities’ categories, along with the time-decay and opinion weight of the binary reputation value are combined to synthesize a overall reputation value. To demonstrate the effectiveness of our system (named BREP), the real transaction data in Taiwan’s ruten on-line auction site are collected to perform experiments. The results show that BREP can provide a more accurate suggestion than the binary reputation value in 10%-24% cases of tests. In particular, for two members with high binary  reputation value, BREP often can differentiate them to help reputation requestor making choice.

誌謝	I
中文摘要	II
英文摘要	III
目錄	IV
圖目錄	VI
表目錄	VII
第1章	緒論	1
第2章	相關技術與背景知識簡介	5
2.1	線上拍賣現況	5
2.2	網路拍賣的交易網路	6
2.3	向量空間模型(VECTOR SPACE MODEL)與相似度(SIMILARITY)計算	8
2.4	名聲計算方法(REPUTATION CALCULATION METHODOLOGIES)	9
第3章	多屬性相對名聲計算方法	11
3.1	拍賣名聲指標	12
3.1.1	商品類別屬性	12
3.1.2	交易金額屬性	14
3.1.3	時間屬性	15
3.2	大眾名聲值的計算(GLOBAL REPUTATION VALUE)	16
第4章	BREP系統	19
4.1	系統架構	19
4.2	系統使用者介面	21
第5章	實驗結果	27
5.1	資料蒐集與實驗設定	28
5.2	非詐騙帳號之服務品質驗證	30
5.3	選擇交易對象之個案探討	33
5.3.1	賣家個案研究	33
5.3.2	買家個案研究	35
第6章	結論與末來展望	37
6.1	結論	37
6.2	未來展望	38
參考文獻	39
附錄A	CSV INPUT	41

圖目錄
圖 2-1 線上拍賣之交易關係圖範例	7
圖 2-2 FUZZYTRUST之交易關系圖說明圖例	9
圖 3-1 多屬性名聲合成示意圖	11
圖 3-2 將交易金額以十分位差來區分並進行正規化	14
圖 3-3 線性時間衰減曲線圖	15
圖 3-4 信任網路範例	18
圖 3-5 多屬性相對名聲模型架構示意圖	18
圖 4-1 BREP系統架構圖	19
圖 4-2 系統初始化	21
圖 4-3 系統啟用	21
圖 4-4 系統偏好設定與計算方法設定	22
圖 4-5 系統使用介面	22
圖 4-6 使用者選定欲交易之商品領域(1)	23
圖 4-7 使用者選定欲交易之商品領域(2)	23
圖 4-8 加入候選交易對象(1)	24
圖 4-9 加入候選交易對象(2)	24
圖 4-10 加入其他候選交易對象	25
圖 4-11 系統建議結果	25
圖 4-12  BREP與露天分數之視覺化圖表	26
圖 5-1 實驗時交易歷史之切割與建議結果之比較	27
圖 5-2 權重組合趨勢圖	32
圖 6-1 BREP專家模式之匯入CSV功能	41

表目錄
表 2-1 本研究與其他文獻之參考之特點比較	10
表 5-1 VBIN與VBREP比較方式	30
表 5-2使用式3-3計算NRV之VBIN, VBREP準確性比較結果	31
表 5-3 使用式3-5計算NRV之VBIN, VBREP準確性比較結果	31
表 5-4 賣家BAUER586888普評之交易紀錄	33
表 5-5 賣家CHRISIC1978負評之交易紀錄	35
表 5-6 賣家MOBILESTAR之正評與普評交易紀錄	36
表 5-7 SAM3227正評之交易紀錄	36
表 6-1 BREP CSV 匯入格式	42
表 6-2 BREP CSV 匯入範例	42

[1]洪儀玶，”具早期預警能力之線上拍賣詐騙偵測”，淡江大學資管系碩士論文，2007年。
[2]Bhattacharjee., R., and A. Goel.j, “Avoiding Ballot Stuffing in eBay-like Reputation Systems,”ACM SIGCOMM’05 workshops, Aug. 22-26, 2005, pp. 133-137.
[3]Brandt, F., “Fundamental Aspects of Privacy and Deception in Electronic Auctions”, Ph.D. Thesis, 2003.
[4]Chang, E., T. Dillon, F.K. Hussain ,“Trust and Reputation for Service-Oriented Environments: Technologies for Building Business intelligence, ” Wiley, May 2007.
[5]Dellarocas, C., “Immunizing online reputation reporting systems against unfair ratings and discrimatory behavior,” Proceedings of the second ACM Conference on Electronic Commerce, October 2000.
[6]Dellarocas, C., “Mechanisms for coping with unfair ratings and discriminatory behaviour in online reputation reporting systems,” International conference on Information Systems, December 2000.
[7]Dellarocas, C., “Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting Mechanisms,” ACM EC’01, Oct. 14-17, 2001, pp. 171-179.
[8]Huynh, D., N. R. Jennings, N. R. Shadbolt, “Developing an Integrated Trust and Reputation Model for Open Multi-Agent Systems,” AAMAS’04 (ACM), pp. 65-74. 
[9]Maximilien, E. M., and M. P. Singh, “Agent-based Trust Model Involving Multiple Qualities,” AAMAS’05 (ACM), July 25-29, 2005, Utrecht, Netherlands.
[10]Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos Faloutsos, “NetProbe: A Fast and Scalable System for Fraud Detection in Online Auction Networks,” WWW 2007, May 8–12, 2007, Banff, Alberta, Canada, pp. 201-210
[11]Ramchurn, S. D., N. R. Jennings, C. Sierra, and L. Dodo, “Devising a trust model for multi-agent interactions using confidence and reputation,” Applied Artificial Intelligence, 18:833-852, 2004. 
[12]Salton, G., and C.Buckley,“Term-Weighting Approaches in Automatic Retrieval,” Information Processing and Management, vol. 24, no. 5, pp. 513-523, 1988.
[13]Song, S.et al., “Trusted P2P Transactions with Fuzzy Reputation Aggregation,” IEEE Internet Computing, Nov-Dec 2005, pp. 24-34.
[14]Yolum, P., and M. P. Singh, “Emergent Properties of referral Systems,” AAMAS’03, July 14-18, 2003, pp. 592-599.
[15]Yu, B.,and M. P. Singh, “An Evidential Model of Distributed Reputation Management,” AAMAS’02 (ACM), July 15-19, 2002, Bologna, Italy. pp. 294-301.
[16]Yu, B., and M. P. Singh, “Detecting Deception in Reputation Management,” AAMAS’03, July 14-18, 2003, pp.73-80.
[17]Yu, B., and M. P. Singh, “Searching Social Networks,” AAMAS’03, July 14-18, 2003, pp. 65-72.
[18]Zhou, R. and K. Hwang, “Trust Overlay Networks for Global Reputation Aggregation in P2P Grid Computing,” IEEE International parallel and Distributed Processing Symposium, Rhodes Island, Grace, April, 2006.