Neural Collaborative Filtering

Pythorch Version of Neural Collaborative Filtering at WWW'17

Paper

Official_Code(Keras)

Author: Dr. Xiangnan He

Keypoints

• The problem that the thesis intends to solve is to recommend the item to the user based on implicit feedback.
• Applying deep learning to user-item interaction in matrix factorization
• Using a network structure that takes advantage of both dot-product (GMF) and MLP
• Use binary cross-entropy rather than MSE as loss function
• Use point-wise loss + negative sampling

Results

Best epoch 005: HR = 0.677, NDCG = 0.399

Requirements

python==3.7.7 pandas==1.0.3 numpy==1.18.1 torch==1.4.0

Reference

• Evaluation Part
• Data Part