Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Predicting the conversion probability for items on C2C ecommerce sites
Proceedings of the 18th ACM conference on Information and knowledge management
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
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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.