Adaptive and automated deep recommender systems

Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU under the advisory of Dr. Jiliang Tang, MS (2017) at USTC and BEng (2014) at UESTC. His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; (2) Urban Computing, Smart City, and GeoAI; (3) Deep Reinforcement Learning, AutoML, and Multimodal ML; and (4) AI for Social Computing, Finance, Education, Ecosystem, and Healthcare. He has published more than 30 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research received ICDM'21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Tencent Open Fund, Criteo Research Award, Bytedance Research Award and MSU Dissertation Fellowship. He serves as top data science conference (senior) program committee members and session chairs (e.g., KDD, AAAI, IJCAI, ICML, ICLR, CIKM), and journal reviewers (e.g., TKDE, TKDD, TOIS, CSUR). He serves as the organizers of DRL4KDD@KDD'19, DRL4IR@SIGIR'20, 2nd DRL4KD@WWW'21, 2nd DRL4IR@SIGIR'21, and a lead tutor at WWW'21/22 and IJCAI'21. He also serves as the founding academic committee members of MLNLP, the largest AI community in China with 800,000 members/followers. The models and algorithms from his research have been launched in the online system of many companies.

[1]  Tong Xu,et al.  AutoField: Automating Feature Selection in Deep Recommender Systems , 2022, WWW.

[2]  Jiliang Tang,et al.  AutoLoss: Automated Loss Function Search in Recommendations , 2021, KDD.

[3]  Jiliang Tang,et al.  UserSim: User Simulation via Supervised GenerativeAdversarial Network , 2021, WWW.

[4]  Jiliang Tang,et al.  AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems , 2021, WWW.

[5]  Yongfeng Zhang,et al.  Towards Long-term Fairness in Recommendation , 2021, WSDM.

[6]  Grace Hui Yang,et al.  Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances , 2020, SIGIR.

[7]  Jiliang Tang,et al.  Automated Embedding Size Search in Deep Recommender Systems , 2020, SIGIR.

[8]  Yulong Gu,et al.  Neural Interactive Collaborative Filtering , 2020, SIGIR.

[9]  Jiliang Tang,et al.  Attacking Black-box Recommendations via Copying Cross-domain User Profiles , 2020, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

[10]  Jiliang Tang,et al.  Jointly Learning to Recommend and Advertise , 2020, KDD.

[11]  Jiliang Tang,et al.  AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations , 2020, ArXiv.

[12]  Jiliang Tang,et al.  DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems , 2019, AAAI.

[13]  Jiliang Tang,et al.  Model-Based Reinforcement Learning for Whole-Chain Recommendations , 2019, ArXiv.

[14]  Jiliang Tang,et al.  "Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator , 2018, SIGWEB Newsl..

[15]  Jiliang Tang,et al.  Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey , 2018 .

[16]  Liang Zhang,et al.  Deep reinforcement learning for page-wise recommendations , 2018, RecSys.

[17]  Liang Zhang,et al.  Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning , 2018, KDD.

[18]  Liang Zhang,et al.  Deep Reinforcement Learning for List-wise Recommendations , 2017, ArXiv.

[19]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[20]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[21]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .