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Personalized Recommendation Multi-Objective Optimization Model Based on Deep Learning


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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. Zhou G Mou N Fan Y Deep interest evolution network for click-through rate prediction . AAAI 2019 , 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. Zamir Amir R Sax Alexander Shen William Guibas Leonidas J Malik Jitendra Savarese Silvio . 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. Chen C Meng X Xu Z Location-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. Wang R Fu B Fu G Deep & cross network for ad click predictions . ADKDD 2017 : 1 7 . Search in Google Scholar

LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature, 2015, 521(7553): 436–444. LeCun Y Bengio Y Hinton G. 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. Song W Shi C Xiao Z Autoint: Automatic feature interaction learning via self-attentive neural networks . CIKM 2019 : 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. Chen Q Zhao H Li W 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 . 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. Liu Shikun Johns Edward Davison Andrew 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. Cheng H T Koc L Harmsen J Wide & 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. Guo H Tang R Ye Y 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 . Search in Google Scholar

Rich Caruana. 1998. Multitask learning. In Learning to learn. Springer, 95–133. Caruana Rich . 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. Chen C Meng X Xu Z Location-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. Rendle S. 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. Ma Jiaqi Zhao Zhe Yi Xinyang Chen Jilin Hong Lichan Chi Ed 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. Tang Hongyan Liu Junning Zhao Ming Gong Xudong . 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). Ma Xiao 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

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2470-8038
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4 veces al año
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Computer Sciences, other