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Extracting and summarizing hot item features across different auction web sites
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Online auctions are generating a new class of fine-grained data about online transactions. This data lends itself to a variety of applications and services that can be provided to both buyers and sellers in online marketplaces. We collect data from online auctions and use several classification algorithms to predict the probable-end prices of online auction items. This paper describes the feature extraction and selection process, and several machine learning formulations of the price prediction problem. As a prototype application, we developed Auction Price Insurance that uses the predicted end-price to offer price insurance to sellers in online auctions. We define Price Insurance as a service that offers insurance to auction sellers that guarantees a price for their goods, for an appropriate premium. If the item sells for less than the insured price, the seller is reimbursed for the difference. We show that our price prediction techniques are accurate enough to offer price insurance as a profitable business. While this paper deals specifically with online auctions, we believe that this is an interesting case study that applies to dynamic markets where the price of the goods is variable and is affected by both internal and external factors that change over time.