recommender-system-implementation

Implement Recommender Systems with Tensorflow/Numpy

FM: Factorization Machines

• paper - Rendle, Steffen. "Factorization machines." 2010 IEEE International Conference on Data Mining. IEEE, 2010.
• paper review - https://ddoeunn.github.io/2020/11/01/PAPER-REVIEW-Factorization-Machines.html
• reference - https://nowave.it/factorization-machines-with-tensorflow.html

LogisticMF: Logistic Matrix Factorization

• paper - Johnson, Christopher C. "Logistic matrix factorization for implicit feedback data." Advances in Neural Information Processing Systems 27 (2014): 78.
• paper review - https://ddoeunn.github.io/2020/11/04/PAPER-REVIEW-Logistic-Matrix-Factorization-for-Implicit-Feedback-Data.html
• reference - https://gist.github.com/tgsmith61591/ce7d614d7a0442f94cd5ae5d1e51d3c2

DeepFM

• paper - Guo, Huifeng, et al. "DeepFM: a factorization-machine based neural network for CTR prediction." arXiv preprint arXiv:1703.04247 (2017).
• paper review - https://ddoeunn.github.io/2020/11/03/PAPER-REVIEW-deepFM.html
• reference - https://github.com/ChenglongChen/tensorflow-DeepFM

NCF : Neural Collaborative Filtering

• paper - He, Xiangnan, et al. "Neural collaborative filtering." Proceedings of the 26th international conference on world wide web. 2017.
• paper review - https://ddoeunn.github.io/2020/11/20/PAPER-REVIEW-Neural-Collaborative-Filtering.html
• reference - https://github.com/hexiangnan/neural_collaborative_filtering

Reference

• original source code of utils microsoft-recommenders