A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Click prediction for product search on C2C web sites
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Proceedings of the 12th ACM conference on Electronic commerce
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
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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.