Open Access
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 03010
Number of page(s) 7
Section Smart Algorithms and Recognition
Published online 04 March 2020
  1. Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. ACM, 2016: 191–198. [Google Scholar]
  2. Huang P S, He X, Gao J, et al. Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 2013: 2333–2338. [Google Scholar]
  3. Rendle S. Factorization machines[C]//2010 IEEE International Conference on Data Mining. IEEE, 2010: 995–1000. [Google Scholar]
  4. Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [Google Scholar]
  5. Zhang W, Du T, Wang J. Deep Learning over Multi-field Categorical Data[J]. 2016. [Google Scholar]
  6. Cheng H T, Koc L, Harmsen J, et al. Wide & Deep Learning for Recommender Systems[J]. 2016. [Google Scholar]
  7. Guo H, Tang R, Ye Y, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [J]. 2017. [Google Scholar]
  8. Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD’17. ACM, 2017: 12. [Google Scholar]
  9. Lian J, Zhou X, Zhang F, et al. xdeepfm: Combining explicit and implicit feature interactions for recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1754–1763. [Google Scholar]
  10. Elkahky A M, Song Y, He X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015: 278–288. [Google Scholar]
  11. Han X, Leung T, Jia Y, et al. Matchnet: Unifying feature and metric learning for patch-based matching[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3279–3286. [Google Scholar]

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