Retrieval of context-aware applications on mobile devices: how to evaluate?
Proceedings of the second international symposium on Information interaction in context
Efficient query expansion for advertisement search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal multi-hierarchy smoothing for estimating rates of rare events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
LASER: a scalable response prediction platform for online advertising
Proceedings of the 7th ACM international conference on Web search and data mining
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Computational advertising is an emerging new scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics. The central challenge of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a search engine ("sponsored search"), a user reading a web page ("content match" and "display ads"), a user watching a movie on a portable device, and so on. The information about the user can vary from scarily detailed to practically nil. The number of potential advertisements might be in the billions. Thus, depending on the definition of "best match" this challenge leads to a variety of massive optimization and search problems, with complicated constraints.