暂无分享,去创建一个
Guibing Guo | Li Shen | Xin Xin | Enneng Yang | G. Guo | Xin Xin | Enneng Yang | Li Shen
[1] Yantao Yu,et al. An Input-aware Factorization Machine for Sparse Prediction , 2019, IJCAI.
[2] Steffen Rendle,et al. Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.
[3] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[4] Tat-Seng Chua,et al. Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.
[5] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[6] J. Weston,et al. Support vector regression with ANOVA decomposition kernels , 1999 .
[7] F. Maxwell Harper,et al. The MovieLens Datasets: History and Context , 2016, TIIS.
[8] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.
[9] Heng-Tze Cheng,et al. Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.
[10] Ge Chen,et al. Interaction-aware Factorization Machines for Recommender Systems , 2019, AAAI.
[11] Gang Fu,et al. Deep & Cross Network for Ad Click Predictions , 2017, ADKDD@KDD.
[12] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[13] Tat-Seng Chua,et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks , 2017, IJCAI.
[14] Bin Yu,et al. Boosting with early stopping: Convergence and consistency , 2005, math/0508276.
[15] Ruocheng Guo,et al. Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression , 2017, SDM.
[16] Philip S. Yu,et al. Multi-view Machines , 2015, WSDM.
[17] Steffen Rendle,et al. Factorization Machines with libFM , 2012, TIST.
[18] Li Shen,et al. A Sufficient Condition for Convergences of Adam and RMSProp , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Xiangnan He,et al. CFM: Convolutional Factorization Machines for Context-Aware Recommendation , 2019, IJCAI.
[20] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[21] Jason Weston,et al. Memory Networks , 2014, ICLR.
[22] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[23] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] AdomaviciusGediminas,et al. Toward the Next Generation of Recommender Systems , 2005 .
[25] Yunming Ye,et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.
[26] A KonstanJoseph,et al. The MovieLens Datasets , 2015 .
[27] Philip S. Yu,et al. Multilinear Factorization Machines for Multi-Task Multi-View Learning , 2017, WSDM.
[28] Naonori Ueda,et al. Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms , 2016, ICML.
[29] Naonori Ueda,et al. Higher-Order Factorization Machines , 2016, NIPS.
[30] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[31] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[32] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[33] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[34] Nuria Oliver,et al. Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild , 2015, ArXiv.