DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

@inproceedings{Guo2017DeepFMAF,
  title={DeepFM: A Factorization-Machine based Neural Network for CTR Prediction},
  author={Huifeng Guo and Ruiming Tang and Yunming Ye and Zhenguo Li and Xiuqiang He},
  booktitle={IJCAI},
  year={2017}
}
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards lowor high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both lowand highorder feature interactions. The proposed model, DeepFM, combines the power of factorization machines for… CONTINUE READING
Highly Cited
This paper has 73 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 26 times over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 44 extracted citations

74 Citations

050201620172018
Citations per Year
Semantic Scholar estimates that this publication has 74 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 38 references

CoRR

  • Yanru Qu, Han Cai, +4 authors Jun Wang. Productbased neural networks for user respon prediction
  • abs/1611.00144,
  • 2016
Highly Influential
7 Excerpts

Similar Papers

Loading similar papers…