Hierarchical rEcurrent Ranking On the Entire Space (HEROES)

This includes tensorflow and pytorch implementations of HEROES model. This is the experiment code in the following work:

Multi-Scale User Behavior Network for Entire Space Multi-Task Learning
Jiarui Jin, Xianyu Chen, Weinan Zhang, Yuanbo Chen, Zaifan Jiang, Zekun Zhu, Zhewen Su, Yong Yu.
CIKM 2022

Prerequisites

Python 3.6
Pytorch 1.8.0
TensorFlow 1.14.0

Run

Please use python model_dataset.py to run our model and baseline methods, where the model is in [esmm, esmm2, mmoe, dnn, heroes], and the dataset is in [alicpp, criteo].

Name		Name	Last commit message	Last commit date
Latest commit History 36 Commits
DARNN		DARNN
DNN		DNN
ESMM		ESMM
ESMM2		ESMM2
Feature_pipeline		Feature_pipeline
HEROES		HEROES
MMOE		MMOE
dupn		dupn
heroes_simple		heroes_simple
hierarchical-rnn		hierarchical-rnn
image		image
motivate_model		motivate_model
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
create.py		create.py
main.py		main.py
reduce.py		reduce.py
remap_sample.zip		remap_sample.zip
test.py		test.py
test_ali.py		test_ali.py
test_criteo.py		test_criteo.py
try.py		try.py

License

Jinjiarui/HEROES

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Hierarchical rEcurrent Ranking On the Entire Space (HEROES)

Prerequisites

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