NDLI: Predicting the conversion probability for items on C2C ecommerce sites

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Predicting the conversion probability for items on C2C ecommerce sites

Content Provider	ACM Digital Library
Author	Wu, Xiaoyuan Bolivar, Alvaro
Abstract	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.
Starting Page	1377
Ending Page	1386
Page Count	10
File Format	PDF
ISBN	9781605585123
DOI	10.1145/1645953.1646127
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ranking Regression model Ecommerce Conversion Online shopping
Content Type	Text
Resource Type	Article

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