Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast online learning through offline initialization for time-sensitive recommendation
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
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Fast top-k retrieval for model based recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
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Computational advertising is an emerging scientific discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, 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 conversing on a cell phone ("mobile advertising"), 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. The main part of this talk will give an introduction to computational advertising and present some illustrative research. In the second part we will discuss connections to recommender systems and present a couple of open problems of potential interest to both communities.