Fetching the paper…
Reading the bibliography…
Designing an effective loss function plays a crucial role in training deep recommender systems.
Autocross: Automatic feature crossing for tabular data in real-world applications. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1936–1945
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. 2019 · 1945
Earlier work this paper cites.
Statistical theory of extreme values and some practical applications: a series of lectures . Vol. 33
Emil Julius Gumbel. 1948 · 1948
Earlier work this paper cites.
Regression
Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, and Brian Marx. 2007 · 2007
Earlier work this paper cites.
Curriculum learning. In Proceedings of the 26th annual international conference on machine learning . 41–48
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Regression analysis by example
Samprit Chatterjee and Ali S Hadi. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
CoSoLoRec: Joint Factor Model with Content, Social, Location for Heterogeneous Point-of-Interest Recommendation. In International Conference on Knowledge Science, Engineering and Management . Springer, 613–627
Hao Guo, Xin Li, Ming He, Xiangyu Zhao, Guiquan Liu, and Guandong Xu. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Large-margin softmax loss for convolutional neural networks.. In ICML , Vol. 2. 7
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016 · 2016
Earlier work this paper cites.
Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
Earlier work this paper cites.
Optimizing intersection-over-union in deep neural networks for image segmentation. In International symposium on visual computing . Springer, 234–244
Md Atiqur Rahman and Yang Wang. 2016 · 2016
Earlier work this paper cites.
A collaborative location based travel recommendation system through enhanced rating prediction for the group of users
Logesh Ravi and Subramaniyaswamy Vairavasundaram. 2016 · 2016
Earlier work this paper cites.
Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . 17–22
Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016 · 2016
Earlier work this paper cites.
Personal recommendation using deep recurrent neural networks in NetEase. In Data Engineering (ICDE), 2016 IEEE 32nd International Conference on . IEEE, 1218–1229
Sai Wu, Weichao Ren, Chengchao Yu, Gang Chen, Dongxiang Zhang, and Jingbo Zhu. 2016a · 2016
Earlier work this paper cites.
Bridging category-level and instance-level semantic image segmentation
Zifeng Wu, Chunhua Shen, and Anton van den Hengel. 2016b · 2016
Earlier work this paper cites.
Exploring the Choice Under Conflict for Social Event Participation. In International Conference on Database Systems for Advanced Applications . Springer, 396–411
Xiangyu Zhao, Tong Xu, Qi Liu, and Hao Guo. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . 1725–1731
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 355–364
Xiangnan He and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
An experimental evaluation of point-of-interest recommendation in location-based social networks
Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017 · 2017
Earlier work this paper cites.
Personalized Deep Learning for Tag Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer
Hanh TH Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, and Lars Schmidt-Thieme. 2017 · 2017
Cited alongside, same era.
BOHB: Robust and efficient hyperparameter optimization at scale. In International Conference on Machine Learning . PMLR, 1437–1446
Stefan Falkner, Aaron Klein, and Frank Hutter. 2018 · 2018
Cited alongside, same era.
xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018 · 2018
Cited alongside, same era.
AutoDis: Automatic Discretization for Embedding Numerical Features in CTR Prediction
Huifeng Guo, Bo Chen, Ruiming Tang, Zhenguo Li, and Xiuqiang He. 2020 · 2020
Later among the works it cites.
Neural input search for large scale recommendation models. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2387–2397
Manas R Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K Adams, Pranav Khaitan, Jiahui Liu, and Quoc V Le. 2020 · 2020
Later among the works it cites.
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, and Ed H Chi. 2020 · 2020
Later among the works it cites.
AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 625–634
Farhan Khawar, Xu Hang, Ruiming Tang, Bin Liu, Zhenguo Li, and Xiuqiang He. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. 2018 · 2018
Cited alongside, same era.
Autoloss: Learning discrete schedules for alternate optimization
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric Xing. 2018 · 2018
Cited alongside, same era.
Distance map loss penalty term for semantic segmentation
Francesco Caliva, Claudia Iriondo, Alejandro Morales Martinez, Sharmila Majumdar, and Valentina Pedoia. 2019 · 2019
Cited alongside, same era.
Spherereid: Deep hypersphere manifold embedding for person re-identification
Xing Fan, Wei Jiang, Hao Luo, and Mengjuan Fei. 2019 · 2019
Cited alongside, same era.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, and James Zou. 2019 · 2019
Cited alongside, same era.
Panoptic feature pyramid networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6399–6408
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár. 2019 · 2019
Cited alongside, same era.
MeLU: meta-learned user preference estimator for cold-start recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Cited alongside, same era.
Am-lfs: Automl for loss function search. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 8410–8419
Chuming Li, Xin Yuan, Chen Lin, Minghao Guo, Wei Wu, Junjie Yan, and Wanli Ouyang. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, and Jifeng Dai. 2020 · 2020
Later among the works it cites.
Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. 2020b · 2020
Later among the works it cites.
Stochastic Loss Function. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 4884–4891
Qingliang Liu and Jinmei Lai. 2020 · 2020
Later among the works it cites.
Towards automated neural interaction discovery for click-through rate prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 945–955
Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, and Xia Hu. 2020 · 2020
Later among the works it cites.
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, and Yan Liu. 2020 · 2020
Later among the works it cites.
Loss function search for face recognition. In International Conference on Machine Learning . PMLR, 10029–10038
Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, and Tao Mei. 2020 · 2020
Later among the works it cites.
AutoHash: Learning Higher-order Feature Interactions for Deep CTR Prediction
Niannan Xue, Bin Liu, Huifeng Guo, Ruiming Tang, Fengwei Zhou, Stefanos P Zafeiriou, Yuzhou Zhang, Jun Wang, and Zhenguo Li. 2020 · 2020
Later among the works it cites.
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long. 2020a · 2020
Later among the works it cites.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang. 2020b · 2020
Later among the works it cites.
FuxiCTR: An Open Benchmark for Click-Through Rate Prediction
Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, and Xiuqiang He. 2020 · 2020
Later among the works it cites.
Neural Interactive Collaborative Filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 749–758
Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, and Dawei Yin. 2020 · 2020
Later among the works it cites.
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et al · 2021
Closest in time.
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang. 2021 · 2021
Closest in time.
Learnable Embedding Sizes for Recommender Systems
Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, and Yong Li. 2021 · 2021
Closest in time.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 750–758
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Hui Liu, and Jiliang Tang. 2021 · 2021
Closest in time.
Self-paced learning with diversity. In Advances in Neural Information Processing Systems . 2078–2086
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, and Alexander Hauptmann. 2014 · 2086
Closest in time.