In the Mood to Click? Towards Inferring Receptiveness to Search Advertising

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...

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Bibliographic Details
Published in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Vol. 1; pp. 319 - 324
Main Authors: Guo, Qi, Agichtein, Eugene, Clarke, Charles L. A., Ashkan, Azin
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Series:ACM Conferences
Subjects:
Advertising
Computer science
Computing methodologies > Machine learning
Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies
Context modeling
Information systems > Information retrieval
Information systems > Information retrieval > Evaluation of retrieval results
Information systems > Information retrieval > Information retrieval query processing
Information systems > Information retrieval > Users and interactive retrieval > Personalization
Information systems > World Wide Web > Web searching and information discovery > Personalization
Intelligent agent
Java
Mice
Mood
Predictive models
Search engines
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Inductive inference
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ISBN:0769538010, 9780769538013
Online Access:Get full text
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Summary: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).
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.368