KDD 2020 Tutorial: Advances in Recommender Systems

Part A: Recommendations in a Marketplace

Rishabh Mehrotra

Rishabh Mehrotra is a Senior Research Scientist at Spotify Research in London. He obtained his PhD in the field of Machine Learning and Information Retrieval from University College London where he was partially supported by a Google Research Award. His PhD research focused on inference of search tasks from query logs and their applications. His current research focuses on bandit based recommendations, counterfactual analysis and experimentation. Some of his recent work has been published at top conferences including WWW, SIGIR, NAACL, CIKM, RecSys and WSDM. Dr. Rishabh has co-taught a number of tutorials at leading conferences.

Ben Carterette

Ben Carterette is a Senior Research Manager at Spotify and an Associate Professor of Computer and Information Sciences at the University of Delaware in Newark, Delaware, USA. His research focuses on evaluation in Information Retrieval, including test collection construction, evaluation measures, and statistical testing. He has published over 70 papers in venues such as ACM TOIS, SIGIR, CIKM, WSDM, ECIR, and ICTIR, winning three Best Paper Awards for his work. In addition, he has co-organized four workshops on IR evaluation and coordinated five TREC tracks. Dr Carterette has recently been elected as the Chair for SIGIR.

Part B: Automated Recommendation System

Yong Li

Yong Li is currently a Tenured Associate Professor of the Department of Electronic Engineering, Tsinghua University. He received the Ph.D. degree in electronic engineering from Tsinghua University in 2012. His research interests include machine learning and big data mining, particularly, automatic machine learning and spatialtemporal data mining for urban computing, recommender systems, and knowledge graphs. He served/is serving as SPC or PC of major Data Mining and AI conferences, including KDD, WWW, IJCAI, AAAI, SIGIR, and UbiComp. He has published over 100 papers on first-tier international conferences and journals, including KDD, WWW, UbiComp, SIGIR, AAAI, TKDE, TMC, etc.

Quanming Yao

Quanming Yao is a senior scientist in 4Paradigm (Hong Kong), who has established and currently is the leader of the company’s machine learning research team. He obtained his Ph.D. degree at the Department of Computer Science and Engineering of Hong Kong University of Science and Technology (HKUST). His research interests are in machine learning, optimization, and automated machine learning. He has 30 top-tier journal and conference papers, including ICML, NeurIPS, JMLR and TPAMI

Chen Gao

Chen Gao is currently a Ph.D. candidate in the Department of Electronic Engineering, Tsinghua University. His research focuses on recommender systems and data mining

James T. Kwok

James T. Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow. He received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology

Isabelle Guyon

Isabelle Guyon is chaired professor in big data at the University Paris-Saclay, specialized in statistical data analysis, pattern recognition and machine learning. She is one of the co-founders of the ChaLearn Looking at People (LAP) challenge series and she pioneered applications of the Microsoft Kinect to gesture recognition. Her areas of expertise include computer vision and and bioinformatics

Qiang Yang

Qiang Yang is a New Bright Endowed Chair Professor of Engineering in Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST). His research interests are artificial intelligence including machine learning and data mining. He is a fellow of AAAI, IEEE, IAPR and AAAS.

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