Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian network framework for reject inference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Online auction has become a very popular e-commerce transaction 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 profit 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 profit before listing and, based on the expected profit, recommend the seller whether to use current auction setting or not. We collect data from five kinds of digital camera from eBay and apply 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 profits using three different approaches: probability-based, end-price based, and our expected-profit based recommendation service. The experiment result shows that our recommendation service based on expected profit gives higher earnings and probability is a key factor that maintains the profit gain when ultra cost incurs for unsold items due to stocking.