This repository contains several state-of-the-art models of recommender system created using the PyTorch framework. The training data used were taken from the public amazon review datasets https://jmcauley.ucsd.edu/data/amazon/.
List of models: 1) Matrix Factorisation (MF) Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer (2009).
2) Bayesian Personalised Ranking (BPRMF) Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452–461, 2009.
3) Collaborative Cross Networks (CoNet) Guangneng Hu, Yu Zhang, and Qiang Yang. Conet: Collaborative cross networks for cross- domain recommendation. In CIKM, pages 667–676, 2018.
4) Cycle Generation Network (CGN) Y Zhang, Y Liu, P Han, C Miao, L Cui, B Li, H Tang: Learning personalized itemset mapping for cross-domain recommendation.In Proceedings of the 29th International Joint Conference on Artificial Intelligence, pages 2561-2567, 2020