In conjunction with 17th SIAM International Conference on Data Mining (SDM 2017)
April 27 - 29, 2017, Houston, Texas, USA
Following the success of the last editions of MLRec in
the third 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), 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
Topics of Interest
We encourage submissions on a variety of topics, including but not limited to:
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,
Feb 1, Feb 7, 2017. The site has started to accept manuscripts.
- Paper Submission:
February 1,February 7, 2017
- Author Notification: February 10, 2017
- Camera Ready Paper Due: February 13, 2017
- Workshop: Apr 29, 2017
David F. Gleich, Purdue University
Title: Deconvolving Feedback Loops in Recommender Systems
Abstract: Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filtering algorithm's predictions over time. We investigate whether it is possible to identify items affected by these feedback loops. We state sufficient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender system's influence on the entire user-item rating matrix. We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user's rated item was recommended or an intrinsic preference. Our results indicate that it is possible to recover the ratings matrix of intrinsic user preferences using a single snapshot of the ratings matrix without any temporal information.
Bio: David Gleich is an assistant professor in the Computer Science Department at Purdue University whose research is on novel models and fast large-scale algorithms for data-driven scientific computing including scientific data analysis and social network analysis. He held the John von Neumann post-doctoral fellowship at Sandia National Laboratories in Livermore CA before joining Purdue in Fall 2011. Gleich received an NSF CAREER Award in 2011 for work on matrix methods for large-scale network analysis. His research is funded by the NSF, DOE, DARPA, and NASA. In 2016, Gleich also received a Sloan Research Fellowship. For more information, see his website: https://www.cs.purdue.edu/homes/dgleich/
Yisong Yue, California Institute of Technology
Title: The Dueling Bandits Problem
Abstract: In this talk, I will present the Dueling Bandits Problem, which is an online learning framework tailored towards real-time learning from subjective human feedback. In particular, the Dueling Bandits Problem only requires pairwise comparisons, which are shown to be reliably inferred in a variety of subjective feedback settings such as for information retrieval and recommender systems. I will provide an overview of the Dueling Bandits Problem with basic algorithmic results. I will then conclude by discussing some ongoing research directions with applications to personalized medicine. This is joint work with Josef Broder, Bobby Kleinberg, Thorsten Joachims, Yanan Sui, Vincent Zhuang, and Joel Burdick.
Bio: Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.
Adith Swaminathan, Microsoft Research
Title: Building Recommenders and Search Engines by Re-using Logged User Feedback
Abstract: Interactive systems like search engines, recommender systems, and ad placement platforms are ubiquitous.Evaluating and optimizing these systems is hard ---users' feedback governs system performance, and gathering their feedback in randomized experiments is costly.I study how we can use logs collected from deployed systems to perform offline evaluation and learning. I will outline two projects (Viewing Recommendations as Treatments, ICML'16 and De-biasing Learning to Rank, WSDM'17) that advance the state of the art for these problems.
Bio: Adith Swaminathan is a researcher in the Deep Learning Technology Center at Microsoft Research Redmond. He completed his PhD at Cornell University advised by Prof. Thorsten Joachims, and received a BTech from IIT Bombay. His graduate thesis developed machine learning algorithms for interactive systems, like search engines and recommender systems, that re-use logs collected from user interactions in a principled manner. Adith's research agenda centers around imbuing current machine learning practice with causal reasoning, so that they can go beyond prediction to intervene reliably in interactive settings.
Suju Rajan, Criteo Research
Bio: Suju Rajan is the VP, Head of Research at Criteo. At Criteo, her team works on all aspects of performance driven computational advertising, including, real-time bidding, large-scale recommendation systems, auction theory, reinforcement learning, online experimentation, metrics and scalable optimization methods. Prior to Criteo, she was the Director of the Personalization Sciences at Yahoo Research where her team worked on personalized recommendations for several Yahoo products. She received her PhD from the University of Texas at Austin, focusing on semi-supervised and active learning based classification for dynamic environments.
Jun Wang, Alibaba
Bio: Dr. Jun Wang is a Director of Engineering at Alibaba Group where he is responsible for leading the development of an intelligent data platform for IOT, and empowering Alibaba’s mobile OS (YunOS) with the company’s robust data management and analytics capabilities. In 2016, his team won the international championship in the prestigious challenge of recommendation system design (RecSys). He is also an adjunct faculty at Columbia University and has taught graduate courses in large scale data analytics. Before joining Alibaba, he worked for IBM T. J. Watson Research and Google. Wang holds a Ph.D. degree from Columbia University in the City of New York.
Xia Ning Email is currently an Assistant Professor at the Department of Computer and Information Science, 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.
Deguang Kong Email is currently a Senior Research Scientist (principal engineer) at Yahoo Research. He worked previously at Samsung Research America, Los Alamos National Lab, NEC research lab, and Penn State University as a researcher. His research interests revolve around machine learning and data science. He has published over 30 referred papers in top conferences, including ICML, NIPS, AAAI, CVPR, KDD, ICDM, SDM, WSDM, CIKM, etc. He has served in program committees of KDD, SDM, AAAI, IJCAI, and reviewed for TDSC, TIFS, TPAMI, TKDE and NIPS. He got two best paper awards: Securecomm 2010 and Samsung best paper 2015.
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
- Mohit Sharma, University of Minnesota, Twin Cities
- Rose Catherine Kanjirathinkal,Carnegie Mellon University
- Jianpeng Xu, Michigan State University
- Lingfei Wu, IBM T.J. Watson Research Center
- Magdalini Eirinaki, San Jose State University
- Fuzhen Zhuang, Chinese Academy of Sciences
- Miao Lu, Yahoo Research
- Yao Wu, Simon Fraser University
Saturday, Apr 29
|9:00am – 9:10am||Workshop opening
|9:10am – 10:10am||Invited talk I: Deconvolving Feedback Loops in Recommender Systems
Speaker: David F. Gleich, Purdue University
|10:10am - 11:10am||Invited talk II:
Speaker: Suju Rajan Criteo Research
|11:10am - 12:10am||
Invited talk III: Building Recommenders and Search Engines by Re-using Logged User Feedback
Speaker: Adith Swaminathan, Microsoft Research
|12:10pm - 1:30pm||
|1:30pm - 2:30pm||
Invited talk IV: The Dueling Bandits Problem
Speaker: Yisong Yue, California Institute of Technology
|2:30pm - 3:30pm||Invited talk V:
Speaker: Jun Wang, Alibaba
|3:30pm - 3:50pm||Contributed paper talk I: Understanding Consumer Behavior with Recurrent Neural Networks
|3:50pm - 4:10pm||Contributed paper talk II: Detecting Meaningful Places and Predicting Locations Using Varied K-Means and Hidden Markov Model
|4:10pm - 4:30pm||Contributed paper talk III: Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network|
|4:30pm - 4:40pm||Contributed paper talk IV: Collaborative filtering for household load prediction given contextual information|
|4:40pm - 4:50pm||Contributed paper talk V: Local Sparse Linear Model Ensemble for Top-N Recommendation|
|4:50pm - 5:10pm||Contributed paper talk VI: Science Driven Innovations for Mobile Data Science: Theory, Practices and Lessons Learned|
|5:10pm - 5:15pm||Workshop closing|
|ID||Title and Authors|
|2||Understanding Consumer Behavior with Recurrent Neural Networks
Tobias Lang, Zalando adtech lab GmbH, Hamburg, Germany
Matthias Rettenmeier, Zalando adtech lab GmbH, Hamburg, Germany
|7||Detecting Meaningful Places and Predicting Locations Using Varied K-Means and Hidden Markov Model
Neelabh Pant, University of Texas at Arlington
Ramez Elmasri, University of Texas at Arlington
|8||Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network
Sungyong Seo, Computer Science Department, University of Southern California
Jing Huang, Visa Research, Visa Inc
Hao Yang, Visa Research, Visa Inc
Yan Liu, Computer Science Department, University of Southern California
|9||Collaborative filtering for household load prediction given contextual information
Taha Hasan, Computer Science Department, Virginia Tech
Naveed Arshad, Computer Science Department, Lahore University of Management Sciences
Erik Dahlquist, Public Technology Department, Malardalen University
Local Sparse Linear Model Ensemble for Top-N Recommendation
Ziwei Fan, Computer and Information Science, Indiana University - Purdue University Indianapolis
Xia Ning, Computer and Information Science, Indiana University - Purdue University Indianapolis
Science Driven Innovations for Mobile Data Science: Theory, Practices and Lessons Learned
Deguang Kang, YAHOO! Research