Fetching the paper…
Reading the bibliography…
Learning effective embedding has been proved to be useful in many real-world problems, such as recommender systems, search ranking and online advertisement.
Embedding-based news recommendation for millions of users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1933–1942
Shumpei Okura, Yukihiro Tagami, Shingo Ono, and Akira Tajima. 2017 · 1942
Earlier work this paper cites.
Nonlinear Programming. In Second Berkeley Symposium on Mathematical Statistics and Probability . 481–492
HW Kuhn and AW Tucker. 1951 · 1951
Earlier work this paper cites.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe. 1956 · 1956
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Social Computing Data Repository at ASU
R. Zafarani and H. Liu. 2009 · 2009
Earlier work this paper cites.
Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
Jean-Antoine Désidéri. 2012 · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. In International Conference on Machine Learning . 427–435
Martin Jaggi. 2013 · 2013
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations . 1–14
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
Deep Graph Embedding for Ranking Optimization in E-commerce. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . ACM, 2007–2015
Chen Chu, Zhao Li, Beibei Xin, Fengchao Peng, Chuanren Liu, Remo Rohs, Qiong Luo, and Jingren Zhou. 2018 · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web . International World Wide Web Conferences Steering Committee, 1067–1077
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . ACM, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems . 3844–3852
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
A survey on network embedding
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018 · 2018
Later among the works it cites.
Real-time personalization using embeddings for search ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 311–320
Mihajlo Grbovic and Haibin Cheng. 2018 · 2018
Later among the works it cites.
Deeper insights into graph convolutional networks for semi-supervised learning. In Thirty-Second AAAI Conference on Artificial Intelligence
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018 · 2018
Later among the works it cites.
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 596–605
Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, Anxiang Zeng, and Luo Si. 2018 · 2018
Later among the works it cites.
Graph attention networks. In International Conference on Learning Representations . 1–12
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
Session-based recommendations with recurrent neural networks. In International Conference on Learning Representations . 1–10
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 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 . 1725–1731
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Representation Learning on Graphs: Methods and Applications
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017b · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations . 1–14
Thomas N Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Cross-Domain Recommendation: An Embedding and Mapping Approach.. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence . 2464–2470
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
Cited alongside, same era.
Sequential Transfer Learning: Cross-domain Novelty Seeking Trait Mining for Recommendation. In Proceedings of the 26th International Conference on World Wide Web Companion . International World Wide Web Conferences Steering Committee, 881–882
Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang, Xiang Ao, Xing Xie, and Qing He. 2017 · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
Billion-scale commodity embedding for e-commerce recommendation in alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 839–848
Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018 · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Learning and Transferring IDs Representation in E-commerce. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 1031–1039
Kui Zhao, Yuechuan Li, Zhaoqian Shuai, and Cheng Yang. 2018 · 2018
Later among the works it cites.
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Later among the works it cites.
Multi-dimensional Graph Convolutional Networks. In Proceedings of the 2019 SIAM International Conference on Data Mining
Yao Ma, Suhang Wang, Charu C Aggarwal, Dawei Yin, and Jiliang Tang. 2019 · 2019
Closest in time.
Solving the Sparsity Problem in Recommendations via Cross-Domain Item Embedding Based on Co-Clustering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . ACM, 717–725
Yaqing Wang, Chunyan Feng, Caili Guo, Yunfei Chu, and Jenq-Neng Hwang. 2019 · 2019
Closest in time.
How Powerful are Graph Neural Networks?. In International Conference on Learning Representations . 1–17
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Closest in time.