Abstract
Millions of dollars turnover is generated every day on popular ecommerce web sites. In China, more than 30 billion dollars transactions were generated from online C2C market in 2009. With the booming of this market, predicting click probability for search results is crucial for user experience, as well as conversion probability. The objective of this paper is to propose a click prediction framework for product search on C2C web sites. Click prediction is deeply researched for sponsored search, however, few studies were reported referred to the domain of online product search. We validate the performance of state-of-the-art techniques used in sponsored search for predicting click probability on C2C web sites. Besides, significant features are developed based on the characteristics of product search and a combined model is trained. Plenty of experiments are performed and the results demonstrate that the combined model improves both precision and recall significantly.
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References
Nick, C., Onno, Z., Michael, T., Bill, R.: An Experimental Comparison of Click Position-bias Models. In: 1st ACM International Conference on Web Search and Data Mining, California (2008)
Regelson, M., Fain, D.: Predicting Click-through Rate Using Keyword Clusters. In: 2nd Workshop on Sponsored Search Auctions (2006)
Matthew, R., Ewa, D., Robert, R.: Predicting Clicks: Estimating the Click-through Rate for New Ads. In: 16th International World Wide Web Conference, Banff (2007)
Haibin, C., Erick, C.: Personalized Click Prediction in Sponsored Search. In: 3rd ACM International Conference on Web Search and Data Mining, Barcelona (2010)
Massimiliano, C., Vanessa, M., Vassilis, P.: Online Learning from Click Data for Sponsored Search. In: 17th International World Wide Web Conference, Beijing (2008)
Gui-Rong, X., Hua-Jun, Z., Zheng, C., Yong, Y., Wei-Ying, M., WenSi, X., WeiGuo, F.: Optimizing Web Search Using Web Click-through Data. In: 13rd ACM International Conference on Information and Knowledge Management, Washington (2004)
Eugene, A., Eric, B., Susan, D.: Improving Web Search Ranking by Incorporating User Behavior Information. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle (2006)
Georges, D., Benjamin, P.: A User Browsing Model to Predict Search Engine Click Data from Past Observations. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore (2008)
Eugene, A., Eric, B., Susan, T.D., Robert, R.: Learning User Interaction Models for Predicting Web Search Result Preferences. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle (2006)
Xiaoyuan, W., Alvaro, B.: Predicting the Conversion Probability for Items on C2C Ecommerce Sites. In: 18th ACM Conference on Information and Knowledge Management, Hong Kong (2009)
David, W.H., Stanley, L.: Applied Logistic Regression. Wiley Series in Probability and Statistics. Wiley-Interscience Publication, Hoboken (2000)
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Wang, X., Liu, C., Xue, G., Yu, Y. (2010). Click Prediction for Product Search on C2C Web Sites. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_38
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DOI: https://doi.org/10.1007/978-3-642-17313-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17312-7
Online ISBN: 978-3-642-17313-4
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