Abstract
Deep neural network (DNN) which is applied to extract high-level features plays an important role in the Click Through Rate (CTR) task. Although the necessity of high-level features has been recognized, how to integrate high-level features with low-level features has not been studied well. There are some works fuse low- and high-level features by simply sum or concatenation operations. We argue it is not an effective way because they treat low- and high-level features equally. In this paper, we propose a novel hybrid feature fusion model named HFF. HFF model consists of two different layers: feature interaction layer and feature fusion layer. With feature interaction layer, our model can capture high-level features. And the feature fusion layer can make full use of low- and high-level features. Comprehensive experiments on four real-world datasets are conducted. Extensive experiments show that our model outperforms existing the state-of-the-art models.
This work was supported in part by the National Key Research and Development Program of China (No. 2018YFB1601102), and the Shenzhen Science and Technology Project under Grant (GGFW2017040714161462).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rendle, S.: Factorization machines. In: ICDM 2010, pp. 995–1000 (2010)
Juan, Y., Zhuang, Y., Chin, W., Lin, C.: Field-aware factorization machines for CTR prediction. In: RecSys 2016, pp. 43–50 (2016)
Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.: Attentional factorization machines: learning the weight of feature interactions via attention networks. In: IJCAI 2017, pp. 3119–3125 (2017)
Song, W., et al.: Autoint: automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3–7 November, pp. 1161–1170 (2019)
Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: xDeepfm: combining explicit and implicit feature interactions for recommender systems. In: ACM SIGKDD 2018, pp. 1754–1763 (2018)
Cheng, H., et al.: Wide & deep learning for recommender systems. In: DLRS@RecSys 2016, pp. 7–10 (2016)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction. In: IJCAI 2017, pp. 1725–1731 (2017)
He, X., Chua, T.: Neural factorization machines for sparse predictive analytics. In: ACM SIGIR 2017, pp. 355–364 (2017)
Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. In: ADKDD 2017, pp. 1–7 (2017)
Vaswani, A., et al.: Attention is all you need. In: NIPS 2017, pp. 6000–6010 (2017)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW 2010, pp. 811–820 (2010)
Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: ACM SIGIR 2011, pp. 635–644 (2011)
Qu, Y., et al.: Product-based neural networks for user response prediction. In: ICDM 2016, pp. 1149–1154 (2016)
Zhang, W., Du, T., Wang, J.: Deep learning over multi-field categorical data. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 45–57. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_4
Shan, Y., Hoens, T.R., Jiao, J., Wang, H., Yu, D., Mao, J.C.: Deep crossing: Web-scale modeling without manually crafted combinatorial features. In: ACM SIGKDD 2016, pp. 255–262 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, Y., Yang, Y. (2020). HFF: Hybrid Feature Fusion Model for Click-Through Rate Prediction. In: Yang, Y., Yu, L., Zhang, LJ. (eds) Cognitive Computing – ICCC 2020. ICCC 2020. Lecture Notes in Computer Science(), vol 12408. Springer, Cham. https://doi.org/10.1007/978-3-030-59585-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-59585-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59584-5
Online ISBN: 978-3-030-59585-2
eBook Packages: Computer ScienceComputer Science (R0)