Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks


  • Yuyu Zhang Chinese Academy of Sciences
  • Hanjun Dai Fudan University
  • Chang Xu Nankai University
  • Jun Feng Tsinghua University
  • Taifeng Wang Microsoft Research
  • Jiang Bian Microsoft Research
  • Bin Wang Chinese Academy of Sciences
  • Tie-Yan Liu Microsoft Research



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




How to Cite

Zhang, Y., Dai, H., Xu, C., Feng, J., Wang, T., Bian, J., Wang, B., & Liu, T.-Y. (2014). Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Machine Learning Applications