ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detecting online commercial intention (OCI)
Proceedings of the 15th international conference on World Wide Web
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
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An empirical analysis of sponsored search performance in search engine advertising
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
To swing or not to swing: learning when (not) to advertise
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the 18th international conference on World wide web
Modeling contextual factors of click rates
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Exploring searcher interactions for distinguishing types of commercial intent
Proceedings of the 19th international conference on World wide web
Understanding users in the wild
Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility
Up or Down? Click-Through Rate Prediction from Social Intention for Search Advertising
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Hi-index | 0.00 |
We present a method for modeling, and automaticallyinferring, the current interest of a user in searchadvertising. Our task is complementary to that of predictingad relevance or commercial intent of a query in the aggregate, since the user intent may vary significantly for the same query. To achieve this goal, we develop a fine-grained user interaction model for inferring searcher receptiveness to advertising. We show that modeling the search context and behavior can significantly improve the accuracy of ad clickthrough prediction for the current user, compared to the existing state-of-the-artclassification methods that do not model this additional session level contextual and interaction information. In particular, our experiments over thousands of search sessions from hundreds of real users demonstrate that our model is more effective at predicting ad clickthrough within the same search session. Our work has other potential applications, such as improving searchinterface design (e.g., varying the number or type of ads) based on user interest, and behavioral targeting (e.g., identifying users interested in immediate purchase).