Awesome

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

Amazon(2014) http://jmcauley.ucsd.edu/data/amazon/links.html
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

<table align="center"> <tr> <th>Dataset</th> <th>number of users</th> <th>number of items</th> <th>MSE of GMF</th> <th>MSE of MLP</th> <th>MSE of NeuMF</th> </tr> <tr> <td><a href="http://files.grouplens.org/datasets/movielens/ml-latest-small.zip">movielens-small</a> (100,836)</td> <td>610</td> <td>9724</td> <td>-</td> <td>-</td> <td>0.740655</td> </tr> <tr> <td>Amazon music-small (64,706)</td> <td>5541</td> <td>3568</td> <td>-</td> <td>-</td> <td>0.822472</td> </tr> <tr> <td>Amazon music (836,006)</td> <td>478235</td> <td>266414</td> <td>-</td> <td>-</td> <td>0.825261</td> </tr> <tr> <td>Amazon Clothing, Shoes and Jewelry (5,748,919)</td> <td>3117268</td> <td>1136004</td> <td>1.520218</td> <td>1.503066</td> <td>1.502135</td> </tr> <tr> <td>Yelp (8,021,121)</td> <td>1968703</td> <td>209393</td> <td>2.222917</td> <td>2.041745</td> <td>2.041674</td> </tr> </table>