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With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018b · 1939
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
Domain adaptation for statistical classifiers
Hal Daume III and Daniel Marcu. 2006 · 2006
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
Earlier work this paper cites.
Convex multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 2008 · 2008
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Online multi-task collaborative filtering for on-the-fly recommender systems. In Proceedings of the 7th ACM conference on Recommender systems . 237–244
Jialei Wang, Steven CH Hoi, Peilin Zhao, and Zhi-Yong Liu. 2013 · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning. In 12th { \{ USENIX } \} symposium on operating systems design and implementation ( { \{ OSDI } \} 16)
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Earlier work this paper cites.
Cross-Stitch Networks for Multi-task Learning. 3994–4003
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016 · 2016
Earlier work this paper cites.
Product-based neural networks for user response prediction. In Proceedings of the16th International Conference on Data Mining . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
Cited alongside, same era.
Learning hidden unit contributions for unsupervised acoustic model adaptation
Pawel Swietojanski, Jinyu Li, and Steve Renals. 2016 · 2016
Cited alongside, same era.
Deep learning over multi-field categorical data. In European conference on information retrieval . Springer
Weinan Zhang, Tianming Du, and Jun Wang. 2016 · 2016
Cited alongside, same era.
Deepfm: a factorization-machine based neural network for ctr prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . Melbourne, Australia., 2782–2788
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Sluice networks: Learning what to share between loosely related tasks
Hierarchical gating networks for sequential recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 825–833
Chen Ma, Peng Kang, and Xue Liu. 2019 · 2019
Later among the works it cites.
Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision . 1406–1415
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. 2019 · 2019
Later among the works it cites.
Multi-source domain adaptation for semantic segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, and Kurt Keutzer. 2019 · 2019
Later among the works it cites.
Deep Interest Evolution Network for Click-Through Rate Prediction. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence . Honolulu, Hawaii, USA, 5941–5948
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
Later among the works it cites.
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Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017 · 2017
Cited alongside, same era.
Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17 . ACM, 12
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Cited alongside, same era.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018
Cited alongside, same era.
Why I like it: multi-task learning for recommendation and explanation. In Proceedings of the 12th ACM Conference on Recommender Systems . 4–12
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018 · 2018
Cited alongside, same era.
Explainable recommendation via multi-task learning in opinionated text data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 165–174
Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018 · 2018
Cited alongside, same era.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on Recommender Systems . 169–177
Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. 2019 · 2019
Cited alongside, same era.
End-to-end multi-task learning with attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1871–1880
Shikun Liu, Edward Johns, and Andrew J Davison. 2019 · 2019
Cited alongside, same era.
Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 5989–5996
Yongchun Zhu, Fuzhen Zhuang, and Deqing Wang. 2019 · 2019
Later among the works it cites.
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Tongwen Huang, Qingyun She, Zhiqiang Wang, and Junlin Zhang. 2020 · 2020
Later among the works it cites.
Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining . 331–339
Pan Li and Alexander Tuzhilin. 2020 · 2020
Later among the works it cites.
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In Proceeding of The 29th ACM International Conference on Information and Knowledge Management . 2685–2692
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Later among the works it cites.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Later among the works it cites.
Multi-source domain adaptation in the deep learning era: A systematic survey
Sicheng Zhao, Bo Li, Pengfei Xu, and Kurt Keutzer. 2020 · 2020
Later among the works it cites.
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4104–4113
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al · 2021
Later among the works it cites.
DCN V2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the Web Conference 2021 . 1785–1797
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021 · 2021
Later among the works it cites.