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Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users.
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. 2018c · 1939
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks. In Proceedings of the 14th international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 315–323
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Earlier work this paper cites.
Click-through rate estimation for rare events in online advertising
Xuerui Wang, Wei Li, Ying Cui, Ruofei Zhang, and Jianchang Mao. 2011 · 2011
Earlier work this paper cites.
No country for old members: User lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web . 307–318
Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Ensemble contextual bandits for personalized recommendation. In Proceedings of the 8th ACM Conference on Recommender Systems . 73–80
Liang Tang, Yexi Jiang, Lei Li, and Tao Li. 2014 · 2014
Earlier work this paper cites.
Beyond clicks: dwell time for personalization. In Proceedings of the 8th ACM Conference on Recommender systems . 113–120
Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014 · 2014
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Dealing with the new user cold-start problem in recommender systems: A comparative review
Le Hoang Son. 2016 · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM international conference on web search and data mining . 565–573
Jiaxi Tang and Ke Wang. 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 . 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.
GeoLifecycle: User engagement of geographical exploration and churn prediction in LBSNs
Young D Kwon, Dimitris Chatzopoulos, Ehsan ul Haq, Raymond Chi-Wing Wong, and Pan Hui. 2019 · 2019
Fairness-aware news recommendation with decomposed adversarial learning. In Proceedings of the 35th AAAI Conference on Artificial Intelligence , Vol. 35. 4462–4469
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021 · 2021
Later among the works it cites.
Modeling the sequential dependence among audience multi-step conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3745–3755
Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021 · 2021
Later among the works it cites.
Deep feedback network for recommendation. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence . 2519–2525
Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. 2021 · 2021
Later among the works it cites.
One person, one model, one world: Learning continual user representation without forgetting. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 696–705
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Cited alongside, same era.
Lifelong sequential modeling with personalized memorization for user response prediction. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 565–574
Kan Ren, Jiarui Qin, Yuchen Fang, Weinan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, et al · 2019
Cited alongside, same era.
Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2810–2818
Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019 · 2019
Cited alongside, same era.
Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2685–2692
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Cited alongside, same era.
Multitask mixture of sequential experts for user activity streams. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 3083–3091
Zhen Qin, Yicheng Cheng, Zhe Zhao, Zhe Chen, Donald Metzler, and Jingzheng Qin. 2020 · 2020
Cited alongside, same era.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems . 269–278
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Cited alongside, same era.
Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 2377–2386
Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, and Keping Yang. 2020 · 2020
Cited alongside, same era.
DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features
Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Yang Chen, Aaron Yi Ding, Xin Wang, and Pan Hui. 2021 · 2021
Cited alongside, same era.
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, and Yudong Li. 2021 · 2021
Later among the works it cites.
Retrospective reader for machine reading comprehension. In Proceedings of the 35th AAAI Conference on Artificial Intelligence , Vol. 35. 14506–14514
Zhuosheng Zhang, Junjie Yang, and Hai Zhao. 2021 · 2021
Later among the works it cites.
A Contrastive Sharing Model for Multi-Task Recommendation. In Proceedings of the 31st international conference on World Wide Web . 3239–3247
Ting Bai, Yudong Xiao, Bin Wu, Guojun Yang, Hongyong Yu, and Jian-Yun Nie. 2022 · 2022
Later among the works it cites.
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck. In IEEE International Conference on Data Engineering
Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022 · 2022
Later among the works it cites.
Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search. In Proceedings of the 16th ACM Conference on Recommender Systems . 350–359
Dilina Chandika Rajapakse and Douglas Leith. 2022 · 2022
Later among the works it cites.
ESCM 2 : Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, and Wei Chu. 2022 · 2022
Later among the works it cites.
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining . 1368–1376
Qianqian Zhang, Xinru Liao, Quan Liu, Jian Xu, and Bo Zheng. 2022 · 2022
Later among the works it cites.
Feedrec: News feed recommendation with various user feedbacks. In Proceedings of the 31st international conference on World Wide Web . 2088–2097
Chuhan Wu, Fangzhao Wu, Tao Qi, Qi Liu, Xuan Tian, Jie Li, Wei He, Yongfeng Huang, and Xing Xie. 2022b · 2097
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