Neural Collaborative Filtering

This is MXNet implementation for the paper:

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). Neural Collaborative Filtering. In Proceedings of WWW '17, Perth, Australia, April 03-07, 2017.

Three collaborative filtering models: Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP), and Neural Matrix Factorization (NeuMF). To target the models for implicit feedback and ranking task, we optimize them using log loss with negative sampling.

Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)

Code Reference: https://github.com/hexiangnan/neural_collaborative_filtering

Environment Settings

We use MXnet with MKL-DNN as the backend.

• MXNet version: MXNet Master(TBD)

Install

pip install -r requirements.txt

Dataset

We provide the processed datasets on Google Drive: MovieLens 20 Million (ml-20m), you can download directly or run the script to prepare the datasets:

python convert.py ./data/

train-ratings.csv

• Train file (positive instances).
• Each Line is a training instance: userID\t itemID\t

test-ratings.csv

• Test file (positive instances).
• Each Line is a testing instance: userID\t itemID\t

test-negative.csv

• Test file (negative instances).
• Each line corresponds to the line of test.rating, containing 999 negative samples.
• Each line is in the format: userID,\t negativeItemID1\t negativeItemID2 ...

Pre-trained models

We provide the pretrained ml-20m model on Google Drive, you can download directly for evaluation or calibration.

dtype	HR@10	NDCG@10
float32	0.6393	0.3849
float32 opt	0.6393	0.3849
int8	0.6395	0.3852
int8 opt	0.6396	0.3852

Training

# train ncf model with ml-20m dataset
python train.py # --gpu=0

Model Optimizer

# optimize nc model
python model_optimizer.py

Calibration

# neumf calibration on ml-20m dataset
python ncf.py --prefix=./model/ml-20m/neumf --calibration
# optimized neumf calibration on ml-20m dataset
python ncf.py --prefix=./model/ml-20m/neumf-opt --calibration

Evaluation

# neumf float32 inference on ml-20m dataset
python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf
# optimized neumf float32 inference on ml-20m dataset
python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-opt
# neumf int8 inference on ml-20m dataset
python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-quantized
# optimized neumf int8 inference on ml-20m dataset
python ncf.py --batch-size=1000 --prefix=./model/ml-20m/neumf-opt-quantized

Benchmark

usage: bash ./benchmark.sh [[[-p prefix ] [-e epoch] [-d dataset] [-b batch_size] [-i instance] [-c cores/instance]] | [-h]]

# neumf float32 benchmark on ml-20m dataset
sh benchmark.sh -p model/ml-20m/neumf
# optimized neumf float32 benchmark on ml-20m dataset
sh benchmark.sh -p model/ml-20m/neumf-opt
# neumf int8 benchmark on ml-20m dataset
sh benchmark.sh -p model/ml-20m/neumf-quantized
# optimized neumf int8 benchmark on ml-20m dataset
sh benchmark.sh -p model/ml-20m/neumf-opt-quantized