Implementation of Neighborhood-based Neural Collaborative Filtering model (NNCF)
Ting Bai et al. "A Neural Collaborative Filtering Model with Interaction-based Neighborhood." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
Parameters:
N_test_negative: the number of negative samples in the testing ranking list
max_neighbors: the maximum neighbors in our algorithm
N_train_negative: the number of negative samples in training
embedding_dim: the output dimension of MLP
nb_layer: the number of layers in MLP
nb_epoch: training epoch
LR: learning rate
01: Process input data: data.csv (userid,itemid)
02: Split train & test set and construct graph
03,04: Construct direct neighbors of model (NNCF_direct)
03-1,04-1: Construct community neighbors of model (NNCF_community)
03-2: Construct knn neighbors of model (NNCF_knn)
05: Training model
06: Evaluation of model
The python files are independent to make our project more flexible and extensible. You can tuning parameters and run the corresponding python file that you need.
Python version: 2.7.3
Keras version：2.1.5
Tensorflow: 1.6.0.
Please cite our paper if you use this code in your own work:
@inproceedings{bai2017neural,
title={A neural collaborative filtering model with interaction-based neighborhood},
author={Bai, Ting and Wen, Ji-Rong and Zhang, Jun and Zhao, Wayne Xin},
booktitle={Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
pages={1979--1982},
year={2017},
organization={ACM}
}