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https://github.com/fajieyuan/SIGIR2021_Conure
Pre-training and Lifelong learning for User Embedding and Recommender System
https://github.com/fajieyuan/SIGIR2021_Conure
catastrophic-forgetting continual-learning foundation-model general-purpose lifelong-learning lifelong-machine-learning one-for-all pre-training self-supervised-learning transfer-learning universal-recommender universal-representation user-profile user-representation
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Pre-training and Lifelong learning for User Embedding and Recommender System
- Host: GitHub
- URL: https://github.com/fajieyuan/SIGIR2021_Conure
- Owner: fajieyuan
- Created: 2021-05-03T06:54:17.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-15T03:42:37.000Z (8 months ago)
- Last Synced: 2024-08-02T13:23:03.548Z (3 months ago)
- Topics: catastrophic-forgetting, continual-learning, foundation-model, general-purpose, lifelong-learning, lifelong-machine-learning, one-for-all, pre-training, self-supervised-learning, transfer-learning, universal-recommender, universal-representation, user-profile, user-representation
- Language: Python
- Homepage:
- Size: 3.44 MB
- Stars: 43
- Watchers: 2
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SIGIR2021_Conure
# One Person, One Model, One World: Learning Continual User Representation without Forgetting
Posts:
https://zhuanlan.zhihu.com/p/437671278
## Pytorch Code is here: https://github.com/yuangh-x/2022-NIPS-Tenrec
🤗 **New Resources**: four Large-scale datasets for evaluating foundation / transferable / cross-domain recommendaiton models.
- **PixelRec**: https://github.com/westlake-repl/PixelRec
- **MicroLens(DeepMind Talk)**: https://github.com/westlake-repl/MicroLens
- **NineRec(TPAMI)**: https://github.com/westlake-repl/NineRec
- **Tenrec(NeurIPS)**: https://github.com/yuangh-x/2022-NIPS-Tenrec
```
@inproceedings{yuan2021one,
title={One person, one model, one world: Learning continual user representation without forgetting},
author={Yuan, Fajie and Zhang, Guoxiao and Karatzoglou, Alexandros and Jose, Joemon and Kong, Beibei and Li, Yudong},
booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={696--705},
year={2021}
}
```
### NextItNet pytorch version: https://github.com/syiswell/NextItNet-Pytorch
### GRec pytorch version: https://github.com/hangjunguo/GRec
---------------------------------------------------
conure_tp_t1.py: Conure is trained on Task1, and once converged it will be pruned.
conure_ret_t1.py: Conure retrains the pruned architecture of Task1
conure_tp_t2.py: Conure is trained on Task2 and once converged it will be pruned.
conure_ret_t2.py: Conure retrains the pruned architecture of Task2
## Large-scale Recommendation Dataset for pretraining，transfer learning (crosss-domain recommendation) and user representation learning：
Download the TTL dataset from: https://drive.google.com/file/d/1imhHUsivh6oMEtEW-RwVc4OsDqn-xOaP/view?usp=sharin
or the ML dataset from: https://drive.google.com/file/d/1-_KmnZFaOdH11keLYVcgkf-kW_BaM266/view?usp=sharing
TTL Dataset: 可用于推荐系统预训练，迁移学习，跨域推荐，冷启动推荐，用户表征学习，自监督学习等任务。
## Running our code:
Put these dataset on Data/Session
FOllowing these steps:
python conure_tp_t1.py After convergence (it takes more than 24 hours for training). We suggest 4 iterations. Parameters will be automatioally saved.
python conure_ret_t1.py You can manually stop this job if the results are satisfied (better than results reported in conure_tp_t1.py). Parameters will be automatioally saved.
python conure_tp_t2.py
python conure_ret_t2.py
python conure_tp_t3.py
python conure_ret_t3.py
python conure_tp_t4.py
python conure_ret_t4.py
## Environments
* Tensorflow (version: 1.10.0)
* python 2.7
## Related work:
```
[1]
@inproceedings{yuan2019simple,
title={A simple convolutional generative network for next item recommendation},
author={Yuan, Fajie and Karatzoglou, Alexandros and Arapakis, Ioannis and Jose, Joemon M and He, Xiangnan},
booktitle={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
pages={582--590},
year={2019}
}
```
```
[2]
@inproceedings{yuan2020parameter,
title={Parameter-efficient transfer from sequential behaviors for user modeling and recommendation},
author={Yuan, Fajie and He, Xiangnan and Karatzoglou, Alexandros and Zhang, Liguang},
booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={1469--1478},
year={2020}
}
```
```
[3]
@inproceedings{yuan2020future,
title={Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation},
author={Yuan, Fajie and He, Xiangnan and Jiang, Haochuan and Guo, Guibing and Xiong, Jian and Xu, Zhezhao and Xiong, Yilin},
booktitle={Proceedings of The Web Conference 2020},
pages={303--313},
year={2020}
}
```
```
[4]
@article{sun2020generic,
title={A Generic Network Compression Framework for Sequential Recommender Systems},
author={Sun, Yang and Yuan, Fajie and Yang, Ming and Wei, Guoao and Zhao, Zhou and Liu, Duo},
journal={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
year={2020}
}
```
```
[5]
@inproceedings{yuan2016lambdafm,
title={Lambdafm: learning optimal ranking with factorization machines using lambda surrogates},
author={Yuan, Fajie and Guo, Guibing and Jose, Joemon M and Chen, Long and Yu, Haitao and Zhang, Weinan},
booktitle={Proceedings of the 25th ACM International on Conference on Information and Knowledge Management},
pages={227--236},
year={2016}
}
```
```
[6]
@article{wang2020stackrec,
title={StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking},
author={Wang, Jiachun and Yuan, Fajie and Chen, Jian and Wu, Qingyao and Li, Chengmin and Yang, Min and Sun, Yang and Zhang, Guoxiao},
journal={arXiv preprint arXiv:2012.07598},
year={2020}
}
```
# Hiring
If you want to work with Fajie https://fajieyuan.github.io/, Please contact him by email [email protected]. His lab is now recruiting visiting students, interns, research assistants, posdocs, and research scientists. You can also contact him if you want to pursue a Phd degree at Westlake University.
Please feel free to talk to him (by weichat: wuxiangwangyuan) if you have ideas or papers for collaboration. He is open to various collaborations.
西湖大学原发杰团队长期招聘：推荐系统和生物信息（尤其蛋白质相关）方向 ，科研助理，博士生，博后，访问学者，研究员系列。