Predicting the conversion probability for items on C2C ecommerce sites

Online ecommerce has been booming for a decade. For instance, as the largest online C2C marketplace (eBay), millions of new items are listed daily. Due to the overwhelming number of items, the process of finding the right items to buy is sometimes daunting. In order to address this problem, this paper describes the idea of predicting the probability that a newly listed item will be sold successfully. And adjust the item exposure chances proportional according to their conversion possibility. Hence, by ranking higher items that users are likely to buy, the chance that users make the purchases could be increased as well as their user satisfaction. For catalog products that have been listed repeatedly, this probability can be measured empirically. However, on C2C sites like eBay, lots of items are not product-based. They are unique, and from different sellers. Therefore, in order to predict whether a new listing will be sold, we collect a large scale item set as the training data, and a set of features were used to model the average buyer shopping decision on C2C sites. Experimental results verified our system's feasibility and effectiveness.

[1]  Thorsten Joachims,et al.  Accurately Interpreting Clickthrough Data as Implicit Feedback , 2017 .

[2]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[3]  Douglas C. Montgomery,et al.  Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics) , 2007 .

[4]  Michael Gamon,et al.  Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis , 2004, COLING.

[5]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[6]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Wei-Ying Ma,et al.  Learning to cluster web search results , 2004, SIGIR '04.

[8]  Wei-Ying Ma,et al.  Optimizing web search using web click-through data , 2004, CIKM '04.

[9]  Daniel C. Fain,et al.  Predicting Click-Through Rate Using Keyword Clusters , 2006 .

[10]  B. Bayus.  Forecasting sales of new contingent products: An application to the compact disc market , 1987 .

[11]  Lluís Màrquez i Villodre,et al.  Boosting Trees for Anti-Spam Email Filtering , 2001, ArXiv.

[12]  Jacob Goldenberg,et al.  From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success , 2004 .

[13]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[14]  Eric Brill,et al.  Improving web search ranking by incorporating user behavior information , 2006, SIGIR.

[15]  Joshua Goodman,et al.  Finding advertising keywords on web pages , 2006, WWW '06.

[16]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[17]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[18]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.

[19]  Oliver Vornberger,et al.  Sales forecasting using neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).