/NeuralCollaborativeFiltering

An implementation using PyTorch for the paper "Neural Collaborative Filtering" (GMF,MLP,NeuMF)

Primary LanguagePython

Neural Collaborative Filtering

This is my 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.

该代码包含GMF、MLP、NeuMF

注：该代码的目标函数和评测指标是均方误差（原文目标函数为交叉熵函数，评测指标为HR和NDCG）。 该代码中模型最后一层没有激活函数（原文中最后一层激活函数为Sigmoid）。

Environments

  • python 3.8
  • pytorch 1.70

Dataset

  1. Amazon(2014) http://jmcauley.ucsd.edu/data/amazon/links.html
  2. Yelp(2020) https://www.yelp.com/dataset

For example: data/ratings_Digital_Music.csv (Amazon Digital Music: rating only)

注：数据集前三列分别为用户id、产品id、评分（1~5）。 运行main.py时，数据集按0.8/0.1/0.1的比例划分为训练集、验证集、测试集。 若使用了amazon/yelp数据集json格式，可使用data_preprocess.py预处理。

For example:

python data_preprocess.py --data_path Digital_Music_5.json --data_source amazon --save_file amazon_music_ratings.csv

Running

Train and evaluate the model

python main.py --dataset_file data/ratings_Digital_Music.csv

Experiment

Dataset number of users number of items MSE of GMF MSE of MLP MSE of NeuMF
movielens-small (100,836) 610 9724 - - 0.740655
Amazon music-small (64,706) 5541 3568 - - 0.822472
Amazon music (836,006) 478235 266414 - - 0.825261
Amazon Clothing, Shoes and Jewelry (5,748,919) 3117268 1136004 1.520218 1.503066 1.502135
Yelp (8,021,121) 1968703 209393 2.222917 2.041745 2.041674