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Zhou G, Mou N, Fan Y, et al. Deep interest evolution network for click-through rate prediction. AAAI 2019, 33: 5941–5948.ZhouGMouNFanYDeep interest evolution network for click-through rate prediction. AAAI2019, 33: 5941–5948.Search in Google Scholar
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Computer Vision and Pattern Recognition, 2018.ZamirAmir RSaxAlexanderShenWilliamGuibasLeonidas JMalikJitendraSavareseSilvio. Taskonomy: Disentangling task transfer learning. In Computer Vision and Pattern Recognition, 2018.Search in Google Scholar
Chen C,Meng X,Xu Z,et al.Location-aware personalized news recommendation with deep semantic analysis.IEEE Access,2017:173–182.ChenCMengXXuZLocation-aware personalized news recommendation with deep semantic analysis.IEEE Access,2017:173–182.Search in Google Scholar
Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions. ADKDD 2017: 1–7.WangRFuBFuGDeep & cross network for ad click predictions. ADKDD2017: 1–7.Search in Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature, 2015, 521(7553): 436–444.LeCunYBengioYHintonG.Deep Learning. Nature, 2015, 521(7553): 436–444.Search in Google Scholar
Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive neural networks. CIKM 2019: 1161–1170.SongWShiCXiaoZAutoint: Automatic feature interaction learning via self-attentive neural networks. CIKM2019: 1161–1170.Search in Google Scholar
Chen Q, Zhao H, Li W, et al. Behavior sequence transformer for e-commerce recommendation in Alibaba. Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. 2019: 1–4.ChenQZhaoHLiWBehavior sequence transformer for e-commerce recommendation in Alibaba. Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. 2019: 1–4.Search in Google Scholar
Shikun Liu, Edward Johns, and Andrew J Davison. 2019. End-to-end multi-task learning with attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1871–1880.LiuShikunJohnsEdwardDavisonAndrew J. 2019. End-to-end multi-task learning with attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Récognition. 1871–1880.Search in Google Scholar
Cheng H T,Koc L,Harmsen J,et al.Wide & deep learning for recommender systems//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.Boston,USA,2016:7–10.ChengH TKocLHarmsenJWide & deep learning for recommender systems//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.Boston,USA,2016:7–10.Search in Google Scholar
Guo H,Tang R,Ye Y,et al.DeepFM:A factorization-machine based neural network for CTR prediction//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia,2017:1725–1731.GuoHTangRYeYDeepFM:A factorization-machine based neural network for CTR prediction//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia,2017:1725–1731.Search in Google Scholar
Rich Caruana. 1998. Multitask learning. In Learning to learn. Springer, 95–133.CaruanaRich. 1998. Multitask learning. In Learning to learn. Springer, 95–133.Search in Google Scholar
Chen C,Meng X,Xu Z,et al.Location-aware personalized news recommendation with deep semantic analysis.IEEE Access,2017:173–182.ChenCMengXXuZLocation-aware personalized news recommendation with deep semantic analysis.IEEE Access,2017:173–182.Search in Google Scholar
Rendle S.Factorization machines//Proceedings of the 2010 IEEE 10th International Conference on Data Mining.Sydney,Australia,2010:995–1000.RendleS.Factorization machines//Proceedings of the 2010 IEEE 10th International Conference on Data Mining.Sydney,Australia,2010:995–1000.Search in Google Scholar
Jiaqi Ma,Zhe Zhao,Xinyang Yi,Jilin Chen,Lichan Hong,Ed H. Chi. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts[P]. Knowledge Discovery & Data Mining,2018: 1930–1939.MaJiaqiZhaoZheYiXinyangChenJilinHongLichanChiEd H.. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts[P]. Knowledge Discovery & Data Mining,2018: 1930–1939.Search in Google Scholar
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In Fourteenth ACM Conference on Recommender Systems (RecSys '20). Association for Computing Machinery, New York, NY, USA, 269–278.TangHongyanLiuJunningZhaoMingGongXudong. 2020. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20). Association for Computing Machinery, New York, NY, USA, 269–278.Search in Google Scholar
Xiao Ma et al. “Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate” International ACM SIGIR Conference on Research and Development in Information Retrieval (2018).MaXiao “Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate” International ACM SIGIR Conference on Research and Development in Information Retrieval (2018).Search in Google Scholar