A tensorflow implementation of our www 2020 paper "LightRec: a Memory and Search-Efficient Recommender System".
You can run run_model.py to test LightRec, and you can run rerank.py to use post-ranking for the model. You can change parameters including learning rate, embedding dimension and so on in params_new.py.
If you refer this code, please please cite the follow paper. @inproceedings{lian2020lightrec, title={LightRec: A Memory and Search-Efficient Recommender System}, author={Lian, Defu and Wang, Haoyu and Liu, Zheng and Lian, Jianxun and Chen, Enhong and Xie, Xing}, booktitle={Proceedings of The Web Conference 2020}, pages={695--705}, year={2020} }