Which used product is more sellable? A time-aware approach

摘要

A number of online marketplaces enable customers to buy or sell used products, which raises the need for ranking tools to help them find desirable items among a huge pool of choices. To the best of our knowledge, no prior work in the literature has investigated the task of used product ranking which has its unique characteristics compared with regular product ranking. While there exist a few ranking metrics (e.g., price, conversion probability) that measure the "goodness'' of a product, they do not consider the time factor, which is crucial in used product trading due to the fact that each used product is often unique while new products are usually abundant in supply or quantity. In this paper, we introduce a novel time-aware metric-"sellability'', which is defined as the time duration for a used item to be traded, to quantify the value of it. In order to estimate the "sellability'' values for newly generated used products and to present users with a ranked list of the most relevant results, we propose a combined Poisson regression and listwise ranking model. The model has a good property in fitting the distribution of "sellability''. In addition, the model is designed to optimize loss functions for regression and ranking simultaneously, which is different from previous approaches that are conventionally learned with a single cost function, i.e., regression or ranking. We evaluate our approach in the domain of used vehicles. Experimental results show that the proposed model can improve both regression and ranking performance compared with non-machine learning and machine learning baselines.