At eBay Market Place, listing conversion rate can be measured by number of items sold divided by number of items in a sample set. For a given item, conversion rate can also be treated as the probability of sale. By investigating eBay listings' transactional patterns, as well as item attributes and user click-through data, we developed conversion models that allow us to predict a live listing's probability of sale. In this paper, we discuss the design and implementation of such conversion models. These models are highly valuable in analysis of inventory quality and ranking. Our work reveals the uniqueness of sales-oriented search at eBay and its similarity to general web search problems.
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