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Title: GitHub - zeroized/pytorch-fm: Factorization Machine models in PyTorch

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MovieLens Datasethttps://grouplens.org/datasets/movielens
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https://patch-diff.githubusercontent.com/zeroized/pytorch-fm#available-models
S Rendle, Factorization Machines, 2010.https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf
W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.https://arxiv.org/abs/1601.02376
HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.https://arxiv.org/abs/1606.07792
J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.https://arxiv.org/abs/1708.04617
X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.https://arxiv.org/abs/1708.05027
X He, et al. Neural Collaborative Filtering, 2017.https://arxiv.org/abs/1708.05031
L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.https://arxiv.org/abs/1902.09096
Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.https://arxiv.org/abs/1611.00144
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.https://arxiv.org/abs/1708.05123
H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.https://arxiv.org/abs/1703.04247
J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.https://arxiv.org/abs/1803.05170
W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.https://arxiv.org/abs/1810.11921
Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, AAAI'20.https://arxiv.org/pdf/1909.03276.pdf
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