RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose including:
- General Recommendation
- Sequential Recommendation
- Context-aware Recommendation
- Knowledge-based Recommendation
The source project at RecBole
This repository,based on Python and Tensorflow, accompanies the semi-synthetic simulation conducted in the paper "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback" by Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata, which has been accepted to WSDM'20.
The source project at Unbiased-Implicit-Rec
This repository,based on Python and Pytorch, is implementation of our WWW'21 paper: Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li, Disentangling User Interest and Conformity for Recommendation with Causal Embedding, In Proceedings of the Web Conference 2021. The source project at DICE
This repository,based on Python and Tensorflow,accompanies the real-world experiments conducted in the paper "Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback" by Yuta Saito, which has been accepted at SIGIR2020 as a full paper.
The source project at Asymmetric-tri-rec