Bayesian Personalized Ranking Neural Network
This repository provides a tensorflow implementation of Neural Bayesian Personalized Ranking from implicit feedback for top-N item recommendation.
Pre-Requisite
Basic Usage
Example
To run Neural Bayesian Personalized Ranking, execute the following command from the project home directory:
python neural_bpr_v1.py 32 128 0.001 0.01 50
Current neural structure is input layer -> embedding layer -> one hidden layer with relu activation function -> output layer with BPR loss
To run the Bayesian Personalized Ranking under Matrix Factorization model, execute the following commend from the project home directory:
python bpr_loss_mf.py 32 0.001 0.01 50
Options
You can check out the hyper-parameter options using:
python neural_bpr_v1.py --help
Dataset
Benchmark MovieLens 1M Dataset (https://grouplens.org/datasets/movielens/)
Output
The output is pairwise ranking loss, HitRate@10, Normalized Discounted Cumulative Gain@10, Area Under Curve (AUC) in each epoch.