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| Title: | 網路拍賣價格哄抬偵測之研究
Detection of Shill Bidding in Online Auctions |
| Authors: | 張成家
Chang, Cheng-Chia |
| Contributors: | 梁定澎
莊皓鈞
Liang, Ting-Peng
Chuang, Hao-Chun
張成家
Chang, Cheng-Chia |
| Keywords: | 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 |
| Date: | 2020 |
| Issue Date: | 2020-09-02 11:47:29 (UTC+8) |
| Abstract: | 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. |
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| Description: | 碩士
國立政治大學
資訊管理學系
107356028 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107356028 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001556 |
| Appears in Collections: | [資訊管理學系] 學位論文
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