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Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
Decision Support And Profit Prediction For Online Auction
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Decision Support And Profit Prediction For Online Auction

978

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Online auction has become a very popular e-commerce trans-action type. The immense business opportunities attract a lot of individuals as well as online stores. With more sellers engaged in, the …

Online auction has become a very popular e-commerce trans-action type. The immense business opportunities attract a lot of individuals as well as online stores. With more sellers engaged in, the competition between sellers is more intense. For sellers, how to maximize their pro¯t by proper auction setting becomes the critical success factor in online auction market. In this paper, we provide a selling recommendation service which can predict the expected pro¯t before listing and, based on the expected pro¯t, recommend the seller whether to use current auction setting or not. We collect data from ¯ve kinds of digital camera from eBay and ap-
ply machine learning algorithm to predict sold probability and end-price. In order to get genuine sold probability and end-price prediction (even for unsold items), we apply probability calibration and sample selection bias correction when building the prediction models. To decide whether to list a commodity or not, we apply cost-sensitive analysis to decide whether to use current auction setting. We compare the pro¯ts using three di®erent approaches: probability-based, end-price based, and our expected-pro¯t based recommendation service. The experiment result shows that our recommendation service based on expected pro¯t gives higher earnings and probability is a key factor that maintains the pro¯t gain when ultra cost incurs for unsold items due to
stocking.

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  • Support Vector Machine(SVM) 是近年來相當流行的分類演算法,並普遍使用於解決 各種分類問題。雖然 SVM 並不直接提供預測的機率值, 不過我們能從資料點至平面 的距離來衡量預測該類別的信心度,並將此信心度轉換正規化至 0~1 區間來代表真實機 率值,但是信心度與真實機率並不一定存在著線性的關係,所以我們無法透過簡單的正 規化公式來進行轉換。
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    • 1. Authors / Chia-Hui Chang and Jun-Hong Lin Presenter / Meng-Lun Wu Decision Support and Profit Prediction for Online Auction Sellers
    • 2. Outline <ul><li>INTRODUCTION </li></ul><ul><li>DATA </li></ul><ul><li>TO SELL OR NOT TO SELL? </li></ul><ul><li>END-PRICE PREDICTION </li></ul><ul><li>SOLD PROBABILITY CALIBRATION </li></ul><ul><li>EXPRIMENTS </li></ul><ul><li>CONCLUSIONS </li></ul>
    • 3. INTRODUCTION <ul><li>Online auction has become a very popular e-commerce transaction type. </li></ul><ul><li>The main issue is to find an auction setting that could maximize sellers profit is a challenging problem. </li></ul><ul><ul><li>profits prediction. </li></ul></ul><ul><li>Ghani and Simmons apply three models for end-price prediction. </li></ul><ul><ul><li>regression, multi-class classification and multiple binary classification. </li></ul></ul>
    • 4. INTRODUCTION (cont.) <ul><li>Some researchers suggest the use of predicted end-price for selling strategy, but they didn’t address how to apply predicted end-price in decision making. </li></ul><ul><li>This paper proposed the use of cost-sensitive decision making to resolve whether to list a commodity or not. </li></ul><ul><li>Authors use profit gain over average profit of similar items sold by similar sellers. </li></ul>
    • 5. DATA <ul><li>Authors collect the digital cameras auction data from eBays, and choose the period of two months in March-April 2006. </li></ul><ul><li>These data are classified into 3 classes: </li></ul><ul><ul><li>Seller Features, Items Features, Auction Features </li></ul></ul>
    • 6. DATA (cont.)
    • 7. DATA (cont.)
    • 8. Two tasks
    • 9. TO SELL OR NOT TO SELL? <ul><li>This paper have three approaches of decision criteria: </li></ul><ul><li>Sold probability Based Approach </li></ul><ul><ul><li>When the predicted sold probability is greater than 50%. </li></ul></ul><ul><ul><ul><li>P(c=1|x) &gt; P(c=-1|x) </li></ul></ul></ul><ul><li>End-price Based Approach </li></ul><ul><ul><li>When the predicted end-price is higher than the average end-price of similar items </li></ul></ul><ul><ul><ul><li>y(x) &gt; Avgy(x) </li></ul></ul></ul>
    • 10. TO SELL OR NOT TO SELL? (cont.) <ul><li>Expected profit Based Approach </li></ul><ul><ul><li>When the expected profit gain for suggesting sell is greater than zero. </li></ul></ul><ul><ul><ul><ul><li>P(c=1|x)[Profit(x) – AvgP(x)]&gt;P(c=-1|x)[lc(x)+uc] </li></ul></ul></ul></ul>
    • 11. END –PRICE PREDICTION <ul><li>There are two types of auction end-price predictions </li></ul><ul><ul><li>Static : Multiple Binary Classification </li></ul></ul><ul><ul><li>Dynamic : Sample Selection Bias Correction </li></ul></ul><ul><li>Ghani and Simmons proposed Multiple Binary Classification prediction method with neural network algorithm get the better accuracy. </li></ul><ul><li>Authors use multiple binary classifiers with two-class estimation to predict the end-price for auction listing items. </li></ul>
    • 12. TWO–CLASS ESTIMATION 47.5
    • 13. Sample Selection Bias Correction <ul><li>Sample selection bias problem has been studied in econometrics and statistics. </li></ul><ul><li>There are four kinds of sample selection bias: </li></ul><ul><ul><li>Complete Independent </li></ul></ul><ul><ul><li>Feature Bias </li></ul></ul><ul><ul><li>Class Bias </li></ul></ul><ul><ul><li>Complete Bias </li></ul></ul>
    • 14. SOLD PROBABILITY CALIBATION <ul><li>To estimation the sold probability for an item, we can use any binary classification to train a model for predicting whether an item would be sold (c=1) or not (c=-1). </li></ul><ul><li>In this paper, we use SVM as our classifier. </li></ul><ul><li>SVM produces an uncalibrated value that is not a probability. </li></ul><ul><li>To yield well calibrated probability, we adopt Platt Scaling to transfer a value f(x i ) into probability P(c=1|x i ). </li></ul>
    • 15. EXPERIMENTS <ul><li>70% data as training data and 30% as testing data. </li></ul><ul><li>The experiments divided into three parts: </li></ul><ul><ul><li>The performance on item sold prediction </li></ul></ul><ul><ul><li>The accuracy of end-price prediction </li></ul></ul><ul><ul><li>Compares three performance of three approaches </li></ul></ul><ul><ul><ul><li>Probability-based </li></ul></ul></ul><ul><ul><ul><li>End-price based </li></ul></ul></ul><ul><ul><ul><li>Expected profit based </li></ul></ul></ul>
    • 16. Accuracy of Sold Probability <ul><li>Authors use SVM as our classifier for predicting. </li></ul><ul><li>The accuracy of prediction whether an item will be sold I about 93.5% and 76.2% for auction listing and BuyItNow listing. </li></ul>
    • 17.
    • 18. END-price Prediction <ul><li>To use multiple binary classifiers for end-price prediction, we need to decide the reference value for each binary classifier. </li></ul><ul><li>Authors exclude the highest and lowest end-prices, and use 10% window of the average end-price as the interval size. </li></ul>
    • 19.
    • 20.
    • 21. Profit Gain Comparison <ul><li>In order to validate that end-price alone does not guarantee high profit due to low sold probability, authors use profit gain over other sellers to compare the three approaches. </li></ul><ul><li>Sell all approach as the baseline for comparison. </li></ul>
    • 22. Auction Listing
    • 23. BuyItNow Listing
    • 24. CONCLUSIONS <ul><li>A critical question for online auction sellers is to find an auction setting. </li></ul><ul><li>Authors apply machine learning algorithms for end-price prediction and sold probability estimation. </li></ul><ul><li>For end-price prediction, since we can only use old items to train the classifiers, the model could under-estimate the end-price for unsold items. </li></ul>
    • 25. CONCLUSIONS (cont.) <ul><li>As shown in the experiments, approaches that don’t involve the consideration of sold probability have deteriorating profit as ultra cost increase. </li></ul><ul><li>This proves our argument that sold probability should also be considered for profit maximization. </li></ul><ul><li>The approach based on both probability and end-price have the best performances among all other approaches. </li></ul>

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