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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9546

    Title: 線上拍賣結標價與期望獲利之預測;Predicting the end-price and sold probability of online auctions for higher profit
    Authors: 林俊宏;Jun-Hong Lin
    Contributors: 資訊工程研究所
    Keywords: 線上拍賣;資料探勘;利潤最大化;Online Auction;Data Mining;Profit Maximization
    Date: 2008-07-16
    Issue Date: 2009-09-22 11:50:16 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 線上拍賣是目前相當流行的商品交易方式, 龐大的商機吸引了大量的一般小型商店或個體相繼投入網拍市場,隨著投入網拍的賣家愈來愈多,賣家彼此之間的競爭也愈趨激烈。此時,對於賣家而言,如何將獲利最大化就成了網拍經營中致勝的關鍵。然而,賣家因為不知該如何達到利潤最大化目標,而被迫採用較易達到的銷售量最大化經營目標及結標價最大化經營目標,導致無法將利潤最大化。在本篇論文中,我們提供一個銷售建議的服務,這個服務能在賣家刊登商品之前,預測商品的利潤高低,藉此建議賣家是否應該以目前所使用的銷售策略來刊登商品,避免賣家使用不適當的銷售策略而導致利潤的損失,進而幫助賣家達到利潤最大化的目標。我們以美國eBay 網站做為資料的收集對象,透過機器學習演算法來預測商品機率及結標價,並考慮銷售策略成本來計算出商品的期望利潤,作為銷售建議服務判斷的依據。此外,在預測商品售出機率及結標 價的議題中,我們解決預測商品售出機率時所發生的機率值偏差問題及結標價預測中可能發生的樣本選擇偏差問題,增加銷售建議服務的獲利能力。最後我們比較銷售量最大化目標、結標價最大化目標及使用銷售建議服務達到利潤最大化目標所獲得的利潤,實驗結果顯示,我們所提供的銷售建議服務能獲取最大的利潤,而採用銷售量最大化目標將導致商品利潤較低,結標價最大化目標容易導致商品無法售出。 Online Auction is a very popular way for trading in the internet. Huge business opportunities attract large numbers of individuals and stores. With the increase of seller, more and more competitions exist in sellers. At this time, how to maximize profit becomes a crucial issue in online auction business. However, achieving profit maximization goal is not an easy task, it obliges seller to adopt more easily achieving sales amount maximization goal or end-price maximization goal, result in less profit. In our work, we provide a novel service which can predict the expected profit of commodity before listing it and then suggest seller should sell it or not. This service can avoid seller to use inappropriate selling strategy, reduce seller’s cost and further achieve profit maximization goal. We collect data from eBay and use machine learning algorithm to predict sold probability and end-price of listing and further consider the cost of selling strategy to estimate the expected profit of commodity as the basis of suggestion. Besides, we also solve the bias problem in sold probability estimation task and the sample selection bias problem in end-price prediction task. After correcting the bias, our service can obtain more profit, especially under some situations. Finally, we compare the profit of sales amount maximization goal, end-price maximization goal and our service. Our simulations show that our service can get highest profit, sales amount maximization goal get fewer profit and end-price maximization goal tend to be without sale.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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