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題名 網路拍賣價格哄抬偵測之研究
Detection of Shill Bidding in Online Auctions作者 張成家
Chang, Cheng-Chia貢獻者 梁定澎<br>莊皓鈞
Liang, Ting-Peng<br>Chuang, Hao-Chun
張成家
Chang, Cheng-Chia關鍵詞 網路拍賣
哄抬物價
出價行為
集群分析
資料探勘
Online Auction
Shill Bidding
Bidding Behavior
Cluster Analysis
Data Mining日期 2020 上傳時間 2-Sep-2020 11:47:29 (UTC+8) 摘要 網路上購物早已成為現代人的習慣，然由於網路上匿名的虛擬帳號，無法確認其身分和過去的交易資訊，使用者得在網路拍賣上進行不當行為，尤其是常見的蓄意哄抬價格，賣家為了謀利，造成買家損失。為了偵測價格哄抬之行為，過去學者透過六個出價行為變數，計算買家可能價格哄抬之機率，變數包含(1)買家參與同一位賣家拍賣之比率；(2)出價次數；(3)得標次數；(4)出價時間；(5)出價增額；(6)進入拍賣的時間。過去研究多使用以上六個行為變數做分群，分群完再計算買家的價格哄抬機率分數，並以平均哄抬機率分數用來評估分群之型態，找出可能價格哄抬集群，此方法衍伸之問題有(1)價格哄抬機率公式之權重為主觀設定，(2)價格哄抬評分與分群為獨立的。本研究提出一個同步評分與分群模型，以整數規劃方法，同時最佳化價格哄抬機率公式權重和分群的組成，透過數據導向的方法，達到分群最佳化，產生的價格哄抬機率，自然地成為每個買家的標籤。使用eBay的真實資料來測試並評估方法的有效性，拍賣網站能根據研究結果，辨識出可能的價格哄抬者，對價格哄抬此一不當行為提出較好的解決辦法，加以防制，保障出價者免於過度付出成本，同時解決過去文獻中，哄抬機率公式權重不一的問題。
Online shopping has become a habit of modern people. Because online accounts are anonymous, they cannot confirm their identity and past transactions. Users can conduct improper behavior in online auctions, especially common deliberate shilling. The sellers’ making a profit will result in the buyers’ losses.In order to detect the behavior of shilling, scholars used six bidding behavior variables to score the probability of shilling. The variables include (1) ratio of bidding to the same seller’s auction, (2) frequency of bidding, (3) number of bids won, (4) Time of bidding, (5) increment of bidding, (6) time to enter the auction. In the past studies, the above six behavioral variables were used for clustering. After clustering, the probability of buyer`s shilling was calculated. Average shilling score of each group was evaluated what kind of type of the group is and find the possible group. This method extends the problem. There are (1) the weights of the formula for the calculating probability of shilling are set subjectively, and (2) scoring probability of shilling and clustering are independent.This research proposes a simultaneous scoring and grouping model, using an integer programming method, while optimizing the weight of the price bidding probability formula and the composition of the groupings. Through a data-oriented method, the grouping optimization is achieved. The resulting price bidding probability naturally becomes labels for each buyer. Use eBay’s real data set to test and evaluate the effectiveness of the model. The auction website can identify possible shilling buyer based on the research results, propose better solutions to prevent them, avoid over-paying costs, and at the same time solve the problem of inconsistent weighting of formulas in the past literature.參考文獻 劉羿君. (2017). 網路拍賣出價行為與價格哄抬之分群研究. 國立中山大學資訊管理學系研究所學位論文楊文菁. (2004). 消費性網路競標策略之影響因素. 國立中山大學傳播管理研究所碩士論文.Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657.Chakraborty, I., & Kosmopoulou, G. (2004). Auctions with shill bidding. Economic Theory, 24(2), 271-287.Chua, C. E. H., & Wareham, J. (2002, December). Self-regulation for online auctions: An analysis. In Proceedings of the Twenty-Third International Conference on Information Systems (pp. 115-125).Dholakia, U. M., Basuroy, S., & Soltysinski, K. (2002). Auction or agent (or both)? A study of moderators of the herding bias in digital auctions. International Journal of Research in Marketing, 19(2), 115-130.Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: a manager`s guide. Addison-Wesley Professional.Kelley, C. T. (1999). Iterative methods for optimization. Society for Industrial and Applied Mathematics.Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for optimization. Mit Press.Krishna, V. (2009). Auction theory. Academic press.Liang, T. P., & Doong, H. S. (2000). Effect of bargaining in electronic commerce. International Journal of Electronic Commerce, 4(3), 23-43.Lucking‐Reiley, D. (2000). Auctions on the Internet: What’s being auctioned, and how?. The journal of industrial economics, 48(3), 227-252.McAfee, R. P., & McMillan, J. (1987). Auctions and bidding. Journal of economic literature, 25(2), 699-738.Runarsson, T. P., & Yao, X. (2000). Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on evolutionary computation, 4(3), 284-294.Steven G. Johnson, The NLopt nonlinear-optimization package, http://ab-initio.mit.edu/nloptTrevathan, J. (2009). Detecting shill bidding in online English auctions. In Handbook of research on social and organizational liabilities in information security (pp. 446-470). IGI Global.Trevathan, J., & Read, W. (2007, April). A simple shill bidding agent. In Fourth International Conference on Information Technology (ITNG`07) (pp. 766-771). IEEE.Trevathan, J., & Read, W. (2007, April). Detecting collusive shill bidding. In Fourth International Conference on Information Technology (ITNG`07) (pp. 799-808). IEEE.Tsang, S., Koh, Y. S., Dobbie, G., & Alam, S. (2014). Detecting online auction shilling frauds using supervised learning. Expert systems with applications, 41(6), 3027-3040.Turban, E. (1997). Auctions and bidding on the Internet: An assessment. Electronic Markets, 7(4), 7-11.Wang, W., Hidvégi, Z., & Whinston, A. B. (2001). Shill bidding in English auctions. Emory University and the University of Texas at Austin, mimeo. 描述 碩士
國立政治大學
資訊管理學系
107356028資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356028 資料類型 thesis dc.contributor.advisor 梁定澎<br>莊皓鈞 zh_TW dc.contributor.advisor Liang, Ting-Peng<br>Chuang, Hao-Chun en_US dc.contributor.author (Authors) 張成家 zh_TW dc.contributor.author (Authors) Chang, Cheng-Chia en_US dc.creator (作者) 張成家 zh_TW dc.creator (作者) Chang, Cheng-Chia en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:47:29 (UTC+8) - dc.date.available 2-Sep-2020 11:47:29 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:47:29 (UTC+8) - dc.identifier (Other Identifiers) G0107356028 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131498 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 107356028 zh_TW dc.description.abstract (摘要) 網路上購物早已成為現代人的習慣，然由於網路上匿名的虛擬帳號，無法確認其身分和過去的交易資訊，使用者得在網路拍賣上進行不當行為，尤其是常見的蓄意哄抬價格，賣家為了謀利，造成買家損失。為了偵測價格哄抬之行為，過去學者透過六個出價行為變數，計算買家可能價格哄抬之機率，變數包含(1)買家參與同一位賣家拍賣之比率；(2)出價次數；(3)得標次數；(4)出價時間；(5)出價增額；(6)進入拍賣的時間。過去研究多使用以上六個行為變數做分群，分群完再計算買家的價格哄抬機率分數，並以平均哄抬機率分數用來評估分群之型態，找出可能價格哄抬集群，此方法衍伸之問題有(1)價格哄抬機率公式之權重為主觀設定，(2)價格哄抬評分與分群為獨立的。本研究提出一個同步評分與分群模型，以整數規劃方法，同時最佳化價格哄抬機率公式權重和分群的組成，透過數據導向的方法，達到分群最佳化，產生的價格哄抬機率，自然地成為每個買家的標籤。使用eBay的真實資料來測試並評估方法的有效性，拍賣網站能根據研究結果，辨識出可能的價格哄抬者，對價格哄抬此一不當行為提出較好的解決辦法，加以防制，保障出價者免於過度付出成本，同時解決過去文獻中，哄抬機率公式權重不一的問題。 zh_TW dc.description.abstract (摘要) Online shopping has become a habit of modern people. Because online accounts are anonymous, they cannot confirm their identity and past transactions. Users can conduct improper behavior in online auctions, especially common deliberate shilling. The sellers’ making a profit will result in the buyers’ losses.In order to detect the behavior of shilling, scholars used six bidding behavior variables to score the probability of shilling. The variables include (1) ratio of bidding to the same seller’s auction, (2) frequency of bidding, (3) number of bids won, (4) Time of bidding, (5) increment of bidding, (6) time to enter the auction. In the past studies, the above six behavioral variables were used for clustering. After clustering, the probability of buyer`s shilling was calculated. Average shilling score of each group was evaluated what kind of type of the group is and find the possible group. This method extends the problem. There are (1) the weights of the formula for the calculating probability of shilling are set subjectively, and (2) scoring probability of shilling and clustering are independent.This research proposes a simultaneous scoring and grouping model, using an integer programming method, while optimizing the weight of the price bidding probability formula and the composition of the groupings. Through a data-oriented method, the grouping optimization is achieved. The resulting price bidding probability naturally becomes labels for each buyer. Use eBay’s real data set to test and evaluate the effectiveness of the model. The auction website can identify possible shilling buyer based on the research results, propose better solutions to prevent them, avoid over-paying costs, and at the same time solve the problem of inconsistent weighting of formulas in the past literature. en_US dc.description.tableofcontents 摘要 iAbstract ii目錄 iii圖目錄 v表目錄 vi第一章 緒論 1第一節 研究背景 1第二節 研究動機 3第三節 研究目的 4第四節 研究流程 5第二章 文獻探討 6第一節 拍賣 6第二節 網路拍賣 9第三節 價格哄抬偵測 12第三章 研究方法 16第一節 導論 16第二節 研究資料 17第三節 同步評分與分群 19第四節 行為變數的計算 22第四章 研究結果與分析 26第一節 買家身分辨識 26第二節 分群結果與哄抬價格公式係數之比較 30第三節 分群行為之分析 37第五章 結論與建議 39第一節 研究結論 39第二節 研究限制 40參考文獻 41附錄 43附錄一 研究資料 43附錄二 Hooke-Jeeves演算法 55 zh_TW dc.format.extent 2708979 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356028 en_US dc.subject (關鍵詞) 網路拍賣 zh_TW dc.subject (關鍵詞) 哄抬物價 zh_TW dc.subject (關鍵詞) 出價行為 zh_TW dc.subject (關鍵詞) 集群分析 zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) Online Auction en_US dc.subject (關鍵詞) Shill Bidding en_US dc.subject (關鍵詞) Bidding Behavior en_US dc.subject (關鍵詞) Cluster Analysis en_US dc.subject (關鍵詞) Data Mining en_US dc.title (題名) 網路拍賣價格哄抬偵測之研究 zh_TW dc.title (題名) Detection of Shill Bidding in Online Auctions en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 劉羿君. (2017). 網路拍賣出價行為與價格哄抬之分群研究. 國立中山大學資訊管理學系研究所學位論文楊文菁. (2004). 消費性網路競標策略之影響因素. 國立中山大學傳播管理研究所碩士論文.Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657.Chakraborty, I., & Kosmopoulou, G. (2004). Auctions with shill bidding. Economic Theory, 24(2), 271-287.Chua, C. E. H., & Wareham, J. (2002, December). Self-regulation for online auctions: An analysis. In Proceedings of the Twenty-Third International Conference on Information Systems (pp. 115-125).Dholakia, U. M., Basuroy, S., & Soltysinski, K. (2002). Auction or agent (or both)? A study of moderators of the herding bias in digital auctions. International Journal of Research in Marketing, 19(2), 115-130.Kalakota, R., & Whinston, A. B. (1997). Electronic commerce: a manager`s guide. Addison-Wesley Professional.Kelley, C. T. (1999). Iterative methods for optimization. Society for Industrial and Applied Mathematics.Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for optimization. Mit Press.Krishna, V. (2009). Auction theory. Academic press.Liang, T. P., & Doong, H. S. (2000). Effect of bargaining in electronic commerce. International Journal of Electronic Commerce, 4(3), 23-43.Lucking‐Reiley, D. (2000). Auctions on the Internet: What’s being auctioned, and how?. The journal of industrial economics, 48(3), 227-252.McAfee, R. P., & McMillan, J. (1987). Auctions and bidding. Journal of economic literature, 25(2), 699-738.Runarsson, T. P., & Yao, X. (2000). Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on evolutionary computation, 4(3), 284-294.Steven G. Johnson, The NLopt nonlinear-optimization package, http://ab-initio.mit.edu/nloptTrevathan, J. (2009). Detecting shill bidding in online English auctions. In Handbook of research on social and organizational liabilities in information security (pp. 446-470). IGI Global.Trevathan, J., & Read, W. (2007, April). A simple shill bidding agent. In Fourth International Conference on Information Technology (ITNG`07) (pp. 766-771). IEEE.Trevathan, J., & Read, W. (2007, April). Detecting collusive shill bidding. In Fourth International Conference on Information Technology (ITNG`07) (pp. 799-808). IEEE.Tsang, S., Koh, Y. S., Dobbie, G., & Alam, S. (2014). Detecting online auction shilling frauds using supervised learning. Expert systems with applications, 41(6), 3027-3040.Turban, E. (1997). Auctions and bidding on the Internet: An assessment. Electronic Markets, 7(4), 7-11.Wang, W., Hidvégi, Z., & Whinston, A. B. (2001). Shill bidding in English auctions. Emory University and the University of Texas at Austin, mimeo. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001556 en_US
