README.md

Outer Product-based Neural Collaborative Filtering

Convolutional Neural Collaborative Filtering performs well based on outer product of user and item embeddings. This is our official implementation for the paper:

Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua, Outer Product-based Neural Collaborative Filtering, In Proceedings of IJCAI'18.

If you use the codes, please cite our paper . Thanks!

Requirements

• Tensorflow 1.7
• numpy, scipy

Quick Start

0. decompress the data files.

   cd Data
   gunzip *
   

1. Pretrain the embeddings using MF_BPR with

   python MF_BPR.py
   

2. Train ConvNCF with pretrained embeddings

   python ConvNCF.py --pretrain=1
   

Dataset

We provide the compressed dataset Yelp(yelp) in Data/

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 999 negative samples. Each line is in the format:

(userID,itemID)\t negativeItemID1\t negativeItemID2 ...

Files

• Data. Training and testing data.
  • yelp.train.rating. Rating of training data.
  • yelp.test.rating. Rating of testing data.
  • yelp.test.negative. 1000 testing samples for each user. (0,32) means this row is for user 0 and the positive test item is 32.
• Dataset.py. Module preprocessing data.
• saver.py. Module saving parameters.
• MF_BPR.py. MF model with BPR loss.
• ConvNCF.py. Our model.