線上商品交易之不誠實賣家偵測

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Title:	線上商品交易之不誠實賣家偵測
Other Titles:	Detection of dishonest sellers for online commodity trading
Authors:	王凱郁;Wang, Kai-Yu
Contributors:	淡江大學資訊管理學系碩士班張昭憲;Chang, Jau-Shien
Keywords:	異常偵測;分類樹;線上購物;電子商務;Anomaly detection;Decision Trees;Online Shopping;Electronic Commerce
Date:	2015
Issue Date:	2016-01-22 14:58:52 (UTC+8)
Abstract:	線上購物改變了現代人在實體商店購物的消費習慣，是電子商務成功的典範之一。然而，購物網站之部分賣家會利用操作或策略的方式來影響評價，以累積信用，提升銷售量。此類包裝評價的行為看似平常，但卻潛藏不安全的交易危機。例如，有許多高評價賣家夾雜販售不良商品，甚至是假貨，讓消費者在不知情狀況下蒙受損失。因此，面對真假參半的評價分數，除了提醒交易者小心謹慎外，需有更積極的因應對策。有鑑於此，本研究針對電子商務網站中的不誠實賣家，發展一套有效的偵測方法。首先，我們歸納蒐集一套龐大的屬性集合，以精確描述不誠實賣家的特質，內容涵蓋評價機制、營業現況、歷史評價與售後服務共105種屬性。其次，我們探討賣家何種方式影響評價建立方式，以決定賣家的分類標籤。接著，再利用x-means群聚演算法對不誠實賣家進行分群，根據群心來了解其各種典型，並據以建立不同的偵測模型。為驗證提出方法之有效性，本研究由淘寶網下在實際交易資料進行驗證。當使用不誠實與誠實做為分類標籤時，平均偵測準確率低於50%。但配合不誠實賣家分群來建立偵測模型時，則準確率可提升至70%~78%，顯示本研究提出方法之可行性。若將不同類型不誠實賣家混合塑模，偵測準確便明顯下降，印證誠實與不誠實賣家之間具有高度相似性。 One of the successful experiences for e-commerce has been online shopping and its changes to the modern consumer patterns in the market. However, some sellers on shopping websites use strategies to impact evaluation, in order to accumulate credit and enhance sales. This impact evaluation appears to be normal, but becomes a potentially unsafe trading crisis. For example, there are many merchandise sellers with high evaluations that will even sell fakes and make consumers suffer unknowingly. When faced with half-truthed evaluation points, traders not only need to be cautious, but also need to have a faster response with better countermeasures. Because of this, this research found an effective method to detect dishonest sellers on e-commerce sites. First, we collected a huge attribute set to accurately describe the nature of dishonest sellers, covering Rating ,Current ,History and Service of 105 kinds of attributes. Second, we explore the many ways sellers impact evaluations in order to classify the ways sellers impact the evaluations. Next, we use x-means clustering algorithm to separate honest sellers and dishonest sellers. According to the cluster centroids, we can analyze the data and create different detection models. To verify the effectiveness of the proposed method, this present study uses actual transaction data from the Taobao for validation. When using attribute sets of dishonest and honest sellers, the average detection rate is less than 50% accurate. But when using attribute sets of only dishonest sellers, the accuracy rate can be increased to 70% to 78%, indicating the feasibility of the method proposed in this study. When mixing different types of methods of dishonest sellers, accurately detecting dishonest sellers decrease significantly. To conclude, honest and dishonest sellers have very similar methods of selling their merchandise.
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