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Reproduction of classical rec models with pytorch like fm, wide&deep, deepfm, dcn, dcnv2, din... (keep updating)

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README

As a beginner, there may be some problems in the code. You are welcome to point them out. If this helps your study, you are also welcome to leave a star:).

Here the dataset is movielens-1m. Feature selection and preprocessing refer to the paper:

FM

ref: Rendle, Steffen. "Factorization machines." 2010 IEEE International conference on data mining. IEEE, 2010.

Wide&Deep

ref: Cheng, Heng-Tze, et al. "Wide & deep learning for recommender systems." Proceedings of the 1st workshop on deep learning for recommender systems. 2016.

DeepFM

ref: Guo, Huifeng, et al. "DeepFM: a factorization-machine based neural network for CTR prediction." arXiv preprint arXiv:1703.04247 (2017).

DCN

ref: Wang, Ruoxi, et al. "Deep & cross network for ad click predictions." Proceedings of the ADKDD'17. 2017. 1-7.

DCNv2

DCNmix

ref: Wang, Ruoxi, et al. "Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems." Proceedings of the web conference 2021. 2021.

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Reproduction of classical rec models with pytorch like fm, wide&deep, deepfm, dcn, dcnv2, din... (keep updating)

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