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Click prediction is one of the fundamental problems in sponsored search. Mostof existing studies took advantage of machine learning approaches to predict adclick for each event of ad view independently. However, as observed in thereal-world sponsored search system, user's behaviors on ads yield highdependency on how the user behaved along with the past time, especially interms of what queries she submitted, what ads she clicked or ignored, and howlong she spent on the landing pages of clicked ads, etc. Inspired by theseobservations, we introduce a novel framework based on Recurrent Neural Networks(RNN). Compared to traditional methods, this framework directly models thedependency on user's sequential behaviors into the click prediction processthrough the recurrent structure in RNN. Large scale evaluations on theclick-through logs from a commercial search engine demonstrate that ourapproach can significantly improve the click prediction accuracy, compared tosequence-independent approaches.

Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks