Recommender System - PyTorch

Implementing recommender system models by using PyTorch.

• Tools
  • Language: Python3.7
  • Modeling Framework: PyTorch
  • Hyper-parameter Optimization Tool: Ray Tune
  • Working Environment: Colab Notebook
• Data
  • MovieLens 100K, Online Advertising
• Code Reference
  • Dive Into Deep Learning/Recommender Systems

1. Matrix Completion on Explicit Feedback

1.1. Matrix Factorization

• Original Paper
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
• Notebook

1.2. SVD++

• Original Paper
  • Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
• Notebook

1.3. SVD++ with Temporal Dynamics

• Original Paper
  • Koren, Y. (2009, June). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 447-456). Chicago
• Notebook

1.4. AutoRec

• Original Paper
  • Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015, May). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web (pp. 111-112). Chicago
• Notebook

1.5. NeuralCF

• Original Paper
  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182). Chicago
• Notebook

2. Personalized Ranking Prediction on Implicit Feedback

2.1. NeuralCF

• Original Paper
  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182). Chicago
• Notebook

2.2. Caser

• Original Paper
  • Tang, J., & Wang, K. (2018, February). Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 565-573). Chicago
• Notebook

3. Click Through Rate (CTR) Prediction on feature-rich interaction data

3.1. Factorization Machines

• Original Paper
  • Rendle, S. (2010, December). Factorization machines. In 2010 IEEE International conference on data mining (pp. 995-1000). IEEE. Chicago
• Notebook

3.2. DeepFM

• Original Paper
  • Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247. Chicago
• Notebook