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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

figure.png

  1. decompress the data files.

    cd Data
    gunzip *
    
  2. Pretrain the embeddings using MF_BPR with

    python MF_BPR.py
    
  3. 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.

About

Experimental codes for paper "Outer Product-based Neural Collaborative Filtering".

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