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Neural Collaborative Filtering

This is our 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.

Please cite our WWW'17 paper if you use our codes. Thanks!

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

Environment Settings

We use Keras with Theano as the backend.

  • Keras version: '1.0.7'
  • Theano version: '0.8.0'

Example to run the codes.

The instruction of commands has been clearly stated in the codes (see the parse_args function).

Run GMF:

python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run MLP:

python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run NeuMF (without pre-training):

python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run NeuMF (with pre-training):

python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrain/ml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrain/ml-1m_MLP_[64,32,16,8]_1501652038.h5

Note on tuning NeuMF: our experience is that for small predictive factors, running NeuMF without pre-training can achieve better performance than GMF and MLP. For large predictive factors, pre-training NeuMF can yield better performance (may need tune regularization for GMF and MLP).

Docker Quickstart

Docker quickstart guide can be used for evaluating models quickly.

Install Docker Engine

Build a keras-theano docker image

docker build --no-cache=true -t ncf-keras-theano .

Example to run the codes with Docker.

Run the docker image with a volume (Run GMF):

docker run --volume=$(pwd):/home ncf-keras-theano python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run the docker image with a volume (Run MLP):

docker run --volume=$(pwd):/home ncf-keras-theano python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run the docker image with a volume (Run NeuMF -without pre-training):

docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1

Run the docker image with a volume (Run NeuMF -with pre-training):

docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrain/ml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrain/ml-1m_MLP_[64,32,16,8]_1501652038.h5
  • Note: If you are using zsh and get an error like zsh: no matches found: [64,32,16,8], should use single quotation marks for array parameters like --layers '[64,32,16,8]'.

Dataset

We provide two processed datasets: MovieLens 1 Million (ml-1m) and Pinterest (pinterest-20).

train.rating:

  • Train file.
  • Each Line is a training instance: userID\t itemID\t rating\t timestamp (if have)

test.rating:

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

test.negative

  • Test file (negative instances).
  • Each line corresponds to the line of test.rating, containing 99 negative samples.
  • Each line is in the format: (userID,itemID)\t negativeItemID1\t negativeItemID2 ...

Last Update Date: December 23, 2018