As the recommendation tasks are getting more diverse and the recommending models are growing more complicated, it is increasingly challenging to develop a proper recommendation system that can adapt well to a new recommendation task. In this tutorial, we focus on how automated machine learning (AutoML) techniques can benefit the design and usage of recommender systems. Specifically, we start from a full scope describing what can be automated for recommendation systems. Then, we elaborate more on three important topics under such a scope, i.e., feature engineering, hyperparameter optimization/neural architecture search, and algorithm selection. The core issues and recent works under these topics will be introduced, summarized, and discussed. Finally, we finalize the tutorial with conclusions and some future directions.
Dates: 2020/08/24 - 4.00-8.00AM (Beijing Time)
The Tutorial is hold online, and is avaliable via Zoom at:
Zoom ID: https://zoom.us/j/99655780785, Password: RnNhVXhhYmZRL0E5Y04rOUM5Uk9ndz09
This tutorial is part of "KDD 2020 Tutorial: Advances in Recommender Systems" (official site).
Speaker: Dr. Quanming Yao (4Paradigm)
Time: 4.00-5.00AM (50mins talk + 10mins QA)
Slides: LINK
Speaker: Prof. Yong Li (Tsinghua)
Time: 5.00-5.40AM (35mins talk + 5mins QA)
Slides: LINK
Speaker: Mr. Chen Gao (Tsinghua)
Time: 5.40-6.20AM (35mins talk + 5mins QA)
Slides: LINK
Speaker: Dr. Huan Zhao (4Paradigm)
Time: 6.20-7.00AM (35mins talk + 5mins QA)
Slides: LINK
Speaker: Dr. Yongqi Zhang (4Paradigm)
Time: 7.00-7.40AM (35mins talk + 5mins QA)
Slides: LINK
- Y. Zhang, Q. Yao Neural Recurrent Structure Search for Knowledge Graph Embedding. International Workshop on Knowledge Graph@KDD. 2020.
- Q. Yao, X. Chen, J. Kwok, Y. Li, C.-J. Hsieh. Efficient Neural Interaction Functions Search for Collaborative Filtering. Webconf. 2020
- Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI. 2020
- Y. Zhang, Q. Yao, W. Dai, L. Chen. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. ICDE. 2020.
- X. Wang, X. He, M. Wang, F. Feng, TS Chua. Neural graph collaborative filtering. SIGIR. 2019.
- Y. Luo, M. Wang, H. Zhou, Q. Yao, W. Tu, Y. Chen, Q. Yang, W. Dai. AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. KDD. 2019.
- Y. Chen, B. Chen, X. He, C. Gao, Y. Li, J.-G. Lou, Y. Wang. LambdaOpt: Learn to Regularize Recommender Models in Finer Levels. KDD. 2019.
- R. Ying, R. He, K. Chen, P. Eksombatchai, W. Hamilton, J. Leskovec. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD. 2019.
- Q. Yao, M. Wang, Y. Li, W. Tu, Q. Yang, Y. Yu. Taking Human out of Learning Applications: A Survey on Automated Machine Learning. Arvix. Nov. 2018.
- H. Zhao, Q. Yao, J. Li, Y. Song, D. Lee. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. KDD. 2017
If you feel the tutorial is helpful, please ack
@inproceedings{10.1145/3394486.3406463,
author = {Mehrotra, Rishabh and Carterette, Ben and Li, Yong and Yao, Quanming and Gao, Chen and Kwok, James and Yang, Qiang and Guyon, Isabelle},
title = {Advances in Recommender Systems: From Multi-Stakeholder Marketplaces to Automated RecSys},
year = {2020},
url = {https://doi.org/10.1145/3394486.3406463},
series = {KDD '20}
}
- Interns, research assistants, and researcher positions are available. See requirement