DIN/DIEN

Implementation based on pytorch for DIN recommendation algorithm

Attention

1. For convenience, referring to authors tensorflow implementation, feature-embedding dimension is identical.
2. Without any L1/L2 normalization or dropout strategy, it's supposed to choose suitable model according to the evaluation stage manually.

File description

file name	description
main.ipynb	Session for training and evaluation
model.py	Defination of target models
DataLoader.py	Self-defined data loader
environment.yml	Conda envrionment yaml

Original paper

Deep Interest Network for Click-Through Rate Prediction

Deep Interest Evolution Network for Click-Through Rate Prediction

Source data

meta_Books.json.gz

reviews_Books.json.gz

Preprocessed data wrapped within data.tar.gz came from mouna99/dien

Reference

mouna99/dien

alibaba/x-deeplearning

shenweichen/DeepCTR-Torch

To do list

• DIN
• AUGRU
• DICE activation layer
• Auxialary loss with neg_sample