Contact Information of Organizers:
Xia Ning, The Ohio State University
Deguang Kong, Yahoo Research
George Karypis, University of Minnesota
Description
The proposed workshop aims to bring the attention of researchers to the various data mining and machine learning methods for recommender systems. This is the fourth edition of the MLRec workshop. We had well-attended first third editions of the workshop with SDM 2015 , SDM 2016 , SDM 2017 and SDM 2018.
Since the inception of recommender systems, there have been a lot of machine learning and data mining algorithms designed for effective and efficient recommendation. To name a few, the matrix factorization techniques [1, 2, 3] are widely used to model the latent space in which users and items interact with each other. The factorization machine [4, 5] uses bilinear regression models [6, 7] to capture the non-linear interactions among the user features and item features. In the past years, researchers have utilized many machine learning techniques such as online learning, metric learning, sparse learning, multi-task learning, and recent deep learning [8, 9, 10] to foster the development of recommender systems.
This workshop focuses on applying novel as well as existing machine learning (including deep learning and reinforcement learning) techniques for improving recommender systems. Some well-established conferences such as NIPS and ICML focus on the study of theoretical properties of machine learning algorithms. On the other hand, the recent developed conference ACM RecSys focuses more on practical aspects of recommender system design and implementation in real-world for industry-level applications. We believe that there is a gap between these two ends, and this workshop aims at bridging the recent advances of machine learning (and/or deep learning) and statistic methodologies (with deeply rooted foundations) to improving recommender systems. In particular, there is an urgent need for researchers from the two communities to jointly work on 1) what are the recent developed machine learning and data mining techniques that can be leveraged to address challenges in recommender systems, and 2) from challenges in recommender systems, what are the practical research directions in the machine learning and data mining community.
We encourage submissions on a variety of topics, including but not limited to:
We believe that advancements on these topics will benefit a variety of algorithm and application domains.
Reference
[1] Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009): 30-37.
[2] Mnih, Andriy, and Ruslan Salakhutdinov. "Probabilistic matrix factorization." Advances in neural information processing systems. 2007.
[3] Weimer, Markus, et al. "Maximum Margin Matrix Factorization for Collaborative Ranking." Advances in neural information processing systems (2007).
[4] Rendle, Steffen. "Factorization machines." Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 2010.
[5] Rendle, Steffen. "Factorization machines with libFM." ACM Transactions on Intelligent Systems and Technology (TIST) 3.3 (2012): 57.
[6] D. C. Montgomery, E. A. Peck, and G. G. Vining. Introduction to linear regression analysis, volume 821. Wiley, 2012.
[7] T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani. The elements of statistical learning, volume 2. Springer, 2009.
[8] M. Trofimov, Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems, ArXiv, 2017, Apr
[9] R. Salakhutdinov, A. Mnih and G. Hinton, Restricted Boltzmann Machines for Collaborative Filtering, ICML, 2017
[10] F. Vasile, E. Smirnova and A. Conneau, Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation, RecSys, 2016
Format
The proposed workshop will have invited talks, oral/poster presentation from paper authors, panel/open discussions.
Length: 1 day (preferred) or half day
Target Audience
The workshop has a board target audience, including but not limited to, machine learning researchers, data mining practitioners, academic researcher and industrial practitioners on recommender systems.
List of Potential Participants
Potential PC Members
Yehuda Koren (Google, USA)
George Karypis (University of Minnesota, USA)
Joseph A. Konstan (University of Minnesota, USA)
Thomas S. Huang (University of Illinois at Urbana-Champaign, USA)
Jieping Ye (University of Michigan, USA)
Zhi-Hua Zhou (Nanjing University, China)
Hui Xiong (Rutgers, the State University of New Jersey, USA)
Qiang Yang (Hong Kong University of Science and Technology, Hong Kong, China)
Chris Ding (University of Taxes, Arlington, USA)
Ronny Lempel (Yahoo Labs, USA)
Chih-Jen Lin (National Taiwan University, Taiwan, China)
Pang-Ning Tan (Michigan State University)
Potential Invited Speakers
Yoelle Maarek, Vice President, Amazon Research
Deepak Agarwal, Director, LinkedIn
George Karypis, Professor, University of Minnesota
Mounia Lalmas, Director, Yahoo Research
DB Tsai, Senior Research Engineer, Netflix
A Summary of Previous Editions
This is the fourth edition of the MLRec workshop. We had well-attended first three editions of the workshop with SDM 2015, SDM 2016, , SDM 2017 and SDM 2018. In MLRec2015, we had 3 contributed papers and 6 invited talks from both academia and industries. In MLRec2016, we had 4 contributed papers and 3 invited talks. In MLRec2017, we had 6 contributed papers and 5 invited talks from both academia and industries. There were approximate 50 attendees in each workshop, which have reached the room capacity. In MLRec2018, we had 5 contributed papers and 6 invited talks from both academia and industries. There were approximate 50 attendees in each workshop, which have reached the room capacity.
Short Bibliography of Organizers
Xia Ning is currently an Assistant Professor at the Department of Biomedical Informatics, and the Computer Science and Engineering, The Ohio State University. Before OSU, she was an Assistant Professor at the Computer Science Deparmtent, Indiana University - Purdue University Indianapolis (IUPUI).
Before joining IUPUI, she was a research staff member at NEC Labs America. Xia received her PhD. degree in Computer Science at University of Minnesota, Twin Cities in 2012. Her research focuses on Recommender Systems, Chemical Informatics and Health Informatics. The results from her research have been presented in various conferences and published in leading peer reviewed journals and highly selective conference proceedings. She has been serving as a program committee member on various premier data mining conferences such as KDD, ICDM and SDM, and Recsys. She has served on the organization panel for MLRec 2015 to 2018.
Deguang Kong is currently a Sr. Research Scientist at Yahoo Research, leading industry-level ads science and recommendation project. He has strong background in machine learning/data mining, and published over 30 referred articles in acadmia conferences (such as ICML, NIPS, AAAI, CVPR, KDD, ICDM, SDM, WSDM, CIKM, etc) and also led mobile data science efforts at Samsung during 2014-2015. He served as a PC member for top conferences and journals, such as KDD, SDM, NIPS, TKDD, etc. Deguang Kong has been on the organization panel for MLRec 2015 to 2018.
George Karypis is currently Professor at the Department of Computer Science and Engineering at the University of Minnesota in the Twin Cities of Minneapolis and Saint Paul and a member of the Digital Technology Center (DTC) at the University of Minnesota. His research interests are concentrated in the areas of bioinformatics, cheminformatics, data mining, and high-performance computing, and from time-to-time, he looks at various problems in the areas of information retrieval, collaborative filtering, and electronic design automation for VLSI CAD. Within these areas, his research focuses in developing novel algorithms for solving important existing and/or emerging problems, and on developing practical software tools implementing some of these algorithms. The results from his research have been presented in various conferences and published in leading peer reviewed journals and highly selective conference proceedings. George Karypis has been on the organization panel for MLRec 2015 to 2018.