sunshinelium/cikm17-nncf

Implementation of A Neural Collaborative Filtering Model with Interaction-based Neighborhood (NNCF)

cikm17-NNCF

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.

Run the model: python main.py

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

File Description

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.

Requirement

Python version: 2.7.3

Keras version：2.1.5

Tensorflow: 1.6.0.

Cite

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}
}