NNCF

Introduction

This repository contains code for paper "On Sampling Strategies for Neural Network-based Collaborative Filtering", which propose (1) a general NNCF framework incorporates both interaction and content information, and (2) sampling strategies for speed up the process.

Model parameters

Loss Functions

pointwise: loss=skip-gram, mse
pairwise: loss=log-loss, max-margin

Content Embedding

CNN: model_choice=cnn_embedding
RNN: model_choice=rnn_embedding
Mean of word vectors: model_choice=basic_embedding

Interaction Module

dot product

Sampling strategies

IID sampling: train_scheme=presample + shuffle_st=random
Negative sampling: train_scheme=original
Stratified sampling: train_scheme=group_sample + shuffle_st=item
Negative sharing: train_scheme=neg_shared
Stratified sampling with negative sharing: train_scheme=group_neg_shared

Other parameters explained

eval_scheme: whole@k for using all test items as candidates, given@-1 for using true items + random sampled 10 items as candidates.
neg_loss_weight & gamma: adjustment in functions
chop_size: number of positive links per item in a batch. Only approximately when set group_shuffling_trick=True (which is recommended).

Setup and Run

unzip data in ./data folder, and go to ./code/sampler, execute ./make.sh
run using scripts under ./code/scripts/demos, which are prepared for each of the sampling strategies.
after running, the results are stored in ./results folder

Requirements

Unix system with python 2.7, GCC 4.8.x and GSL
Keras 1.2.2
Tensorflow 1.0

The code may or may not be working properly with other versions.

Tips

GSL can be installed with sudo apt-get install libgsl-dev
Keras can be installed by first downloading zip file of version 1.2.2 and then installing with command python setup.py install

Cite

@inproceedings{chen2017onsampling,
    title={On Sampling Strategies for Neural Network-based Collaborative Filtering},
    author={Chen, Ting and Sun, Yizhou and Shi, Yue and Hong, Liangjie},
    booktitle={Proceedings of the 23th ACM SIGKDD international conference on Knowledge discovery and data mining},
    year={2017},
    organization={ACM}
}