以資料清潔為基礎的拍賣決策輔助系統

近年來，在拍賣網站上做競標已經是非常普遍的購物行為，競標者希望能從中找到便宜的商品做競標。但是因為商品數目太過眾多，使用者需要花很多時間做比較的動作。當使用者輸入想搜尋的商品關鍵字之後，拍賣網站所回傳的符合商品，其實只有很小的比例是使用者真正想要的，這種情況在3C產品類別特別嚴重。我們研究的目標是讓使用者透過我們的系統：CADBid，能夠很有效率地做商品比價的動作。CADBid是一個以web為介面的系統，可以自動對拍賣網站的商品做一個商品過濾的動作，並且將商品的詳細敘述做清潔，最後呈現給使用者一個產品列表，裡面列出他感興趣的商品及其重要產品屬性。我們的研究著重在兩項工作：拍賣商品的過濾及產品敘述的清潔，我們並利用清潔後的產品敘述輔助產品過濾。我們將這兩項工作用分類的方式來處理，並訂出了分類所需的屬性集(feature set)。我們根據這些屬性集，以支撐向量機(Support Vector Machine)建立分類模型來解決我們的問題。從我們的實驗可看出敘述清潔這個動作是有幫助的，因為清潔後的敘述確實提升了產品過濾的準確度，讓拍賣過濾也能達到更好的效果。使用者從透過網路直接使用CADBid的輔助功能，可以很容易地對拍賣產品做比較、比價，而得到很好的線上購物經驗。

關鍵字

拍賣網站；資料擷取；分類

並列摘要

Bidding for products on the Internet has become a common activity in our daily life. However, it’s a tedious problem that there are too many items for the bidder to select the cheapest one. In the results providing by eBay, only a small number of results are target items. This is a common situation while the user is searching for a main product in 3C. We aim at helping the bidder compare items easily on auction websites. In this thesis we propose CADBid, which is a web-based system built between auction websites and the bidder. CADBid is able to automatically filter out non-target items and clean the descriptions about these items. Afterward, a list is generated which helps the bidder compare these items. The list only shows the target items along with their important properties. Our work focuses on two tasks. The first task is item filtering. The second is cleaning of descriptions. After cleaning of descriptions, the clean descriptions are used to assist the first task. We view the two tasks as classification problems and propose two feature sets. We build two classification models based on Support Vector Model. Our experiment shows that cleaning of description is helpful because clean descriptions indeed improve the accuracy of item filtering. With CADBid, the bidder will be convenient while making a good decision on which item to bid.

並列關鍵字

classification ； auction ； information extraction ； cleaning

參考文獻

[1] R. Benjamin, "Electronic Markets and Virtual Value Chains on the Information Superhighway," Sloan Management Review, pp. 62-71, 1995.

[9] S. Debnath, P. Mitra, N. Pal, and C. L. Giles, “Automatic Identification of Informative Sections of Web Pages,” IEEE Transactions on Knowledge and Data Engineering 17, 9, Sep. 2005.

[10] V. Vapnik, Statistical Learning Theory. New York: Springer Verlage, 1998.

[11] B. D. Robert, E. Oren, and S. W. Daniel, "A Scalable Comparison-shopping Agent for the World-Wide Web," in Proceedings of the first international conference on Autonomous agents Marina del Rey, California, United States: ACM Press, 1997.

[12] T. Ito, N. Fukuta, T. Shintani, and K. Sycara, "BiddingBot: A Multiagent Support System for Cooperative Bidding in Multiple Auctions," in Proceedings of ICMAS2000, 2000, pp. 435-436.

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以資料清潔為基礎的拍賣決策輔助系統

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