BPR pytorch
A pytorch implementation for BPR (Bayesian Personalized Ranking).
Note that I use the two sub datasets provided by Xiangnan's [repo](https://github.com/hexiangnan/neural_collaborative_filtering/tree/master/Data). Another pytorch NCF implementaion can be found at this [repo](https://github.com/guoyang9/NCF). The project is written primarily in Python, first published in 2019. Key topics include: bayesian-personalized-ranking, bpr, pytorch, recommender-system.
Pytorch-BPR
Note that I use the two sub datasets provided by Xiangnan's repo. Another pytorch NCF implementaion can be found at this repo.
I utilized a factor number 32, and posted the results in the NCF paper and this implementation here. Since there is no specific numbers in their paper, I found this implementation achieved a better performance than the original curve. Moreover, the batch_size is not very sensitive with the final model performance.
| Models | MovieLens HR@10 | MovieLens NDCG@10 | Pinterest HR@10 | Pinterest NDCG@10 |
|---|---|---|---|---|
| pytorch-BPR | 0.700 | 0.418 | 0.877 | 0.551 |
The requirements are as follows:
* python==3.6
* pandas==0.24.2
* numpy==1.16.2
* pytorch==1.0.1
* tensorboardX==1.6 (mainly useful when you want to visulize the loss, see https://github.com/lanpa/tensorboard-pytorch)
Example to run:
python main.py --factor_num=16 --lamda=0.001
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