MLRec 2019

5th International Workshop on Machine Learning Methods for Recommender Systems

In conjunction with 19th SIAM International Conference on Data Mining (SDM 2019)
May 2 - 4, 2019, Calgary, Alberta, USA


Following the success of the several editions of MLRec in 2015, 2016, 2017, and 2018, the fifth edition of the MLRec workshop focuses on developing novel, and applying existing Machine Learning (ML) and Data Mining (DM) methods to improve recommender systems. This workshop also highly encourages applying ML-based recommendation algorithms in novel application domains (e.g., precision medicine), deep learning for recommendation, and solving novel recommendation problems formulated from industry. The ultimate goal of the MLRec workshop series is to promote the advancement and implementation of new, effective and efficient ML and DM techniques with high translational potential for real and large-scale recommender systems, and to expand the territory of ML-based recommender system research toward non-conventional application areas where recommendation problems largely exist but haven't been fully recognized.

Topics of Interest

We encourage submissions on a variety of topics, including but not limited to:

  • Novel machine learning algorithms for recommender systems, e.g., new content-based or context-aware recommendation algorithms, new algorithms for matrix factorization, tensor-based approaches for recommender systems, etc.
  • Novel applications of existing machine learning and data mining algorithms for recommender systems, e.g., applying bilinear models, (non-convex) sparse learning, metric learning, low-rank approximation/PCA/SVD, neural networks and deep learning, etc.
  • Novel optimization techniques for improving recommender systems, e.g., parallel/distributed optimization techniques, efficient stochastic gradient descent, etc.
  • Industrial practices and implementations of recommendation systems, e.g., feature engineering, model ensemble, large-scale implementations of recommender systems, etc.
  • Emerging recommendation problems and scenarios in industry and their ML-based solutions, e.g., recommendation for e-fashion, etc.
  • Novel recommendation problems in non-conventional recommender system research areas (e.g., precision medicine, health informatics) and their ML-based solutions, e.g., recommendation of physicians, recommendation of healthy life-styles for seniors, etc.
  • Enhanced deep learning methods for recommender systems, e.g., word embedding techniques, CNN, RNN and LSTM, Generative Advertiseral Networks (GAN), auto-encoder, RBM, etc.
  • Recommendation in Information Retrieval, ad industry, targeting ad, search ad, etc.



The workshop accepts long paper and short (demo/poster) papers. Short papers submitted to this workshop should be limited to 4 pages while long papers should be limited to 8 pages. All papers should be formatted using the SIAM SODA macro. Authors are required to submit their papers electronically in PDF format to the submission site by 11:59pm MDT, March 31, 2019. The site has started to accept manuscripts. At least one author of each accepted paper should be registered to the conference.

Important Dates

  • Paper Submission Deadline: March 31, 2019
  • Author Notification: Apr 5, 2019
  • Camera Ready Paper Due: Apr 15, 2019
  • Workshop: May 4, 2018

Invited Speakers


Organizing Committee

Xia Ning Xia Ning Email is currently an Assistant Professor at the Biomedical Informatics Department, The Ohio State University. Before joining OSU, she was an Assistant Professor at the Indiana University - Purdue University Indianapolis (IUPUI), and 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.

Deguang Kong Deguang Kong Email is currently a Sr. Research Scientist at Yahoo Research, leading industry-level ad 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 MLRec2015, MLRec 2016, MLRec 2017 and MLRec 2018.

George Karypis George Karypis Email is currently Professor at the Department of Computer Science & 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


Accepted Papers