MLRec 2016

2nd International Workshop on Machine Learning Methods for Recommender Systems

In conjunction with 16th SIAM International Conference on Data Mining (SDM 2016)
Saturday, May 7, 2016, Miami, Florida, USA

Following the success of the last edition of MLRec, this second edition focuses on applying novel as well as existing machine learning and data mining methodologies for improving recommender systems. There are many established conferences such as NIPS and ICML that focus on the study of theoretical properties of machine learning algorithms. On the other hand, the recent developed conference ACM RecSys focuses on different aspects of designing and implementing recommender systems. We believe that there is a gap between these two ends, and this workshop aims at bridging the recent advances of machine learning and data mining algorithms to improving recommender systems. Since many recommendation approaches are built upon data mining and machine learning algorithms, these approaches are deeply rooted in their foundations. As such, 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.

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/context aware recommendation algorithms, new algorithms for matrix factorization handling cold-start items, tensor-based approach for recommender systems, and etc.
  • Novel approaches for applying existing machine learning algorithms, e.g., applying bilinear models, (non-convex) sparse learning, metric learning, low-rank approximation/PCA/SVD, neural networks and deep learning, for recommender systems.
  • Novel optimization algorithms and analysis for improving recommender systems, e.g., parallel/distributed optimization techniques and efficient stochastic gradient descent.
  • Industrial practices and implementations of recommendation systems, e.g., feature engineering, model ensemble, and lessons from large-scale implementations of recommender systems.
  • Machine learning methods for security and privacy aware recommendations, user-centric recommendations with emphasize on users’ interaction and engagement, Explore-Exploit approach, multi-armed bandits for recommendation, and 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, Feb 1, 2016. The site has started to accept manuscrips.

Important Dates

  • Paper Submission: February 1, 2016
  • Author Notification: February 10, 2016
  • Camera Ready Paper Due: February 15, 2016 s
  • Workshop: Saturday, May 7, 2016

Invited Speakers

Tina Eliassi-Rad Tina Eliassi-Rad, Rutgers University
Title: Use of Social Networks in Recommendation Systems
Abstract: Consider the problem of incorporating social network information into a recommendation system. Intuitively solving this problem should improve the recommender’s performance. In this talk, I will discuss the challenges, present the current state-of-the-art, and outline some promising directions for this problem.
Bio: Tina Eliassi-Rad is an Associate Professor of Computer Science at Rutgers University. Before joining academia, she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her current research lays at the intersection of graph mining, network science, and computational social science. Within data mining and machine learning, Tina's research has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, complex networks, fraud detection, and cyber situational awareness. She has published over 60 peer-reviewed papers (including a best paper runner-up award at ICDM'09 and a best interdisciplinary paper award at CIKM'12); and has given over 120 invited presentations. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science. For more details, visit here.

Julian McAuley Julian McAuley, University of California, San Diego
Title: Harnessing reviews to build richer models of opinions
Abstract: Online reviews are often our first port of call when considering products and purchases online. Yet navigating huge volumes of reviews (many of which we might disagree with) is laborious, especially when we are interested in some niche aspect of a product. This suggests a need to build models that are capable of capturing the complex and idiosyncratic semantics of reviews, in order to build richer and more personalized recommender systems. In this talk I'll discuss three such directions: First, how can reviews be harnessed to better understand the dimensions (or facets) of people's opinions? Second, how can reviews be used to answer targeted questions, that may be subjective or require personalized responses? And third, how can reviews themselves be synthesized, so as to predict what a reviewer would say, even for products they haven't seen yet?
Bio: Julian McAuley’s research focuses on the linguistic, temporal, and visual dimensions of opinions and behavior in social networks and online communities. This includes understanding the facets of people's opinions, the processes by which people "acquire tastes" for gourmet foods and beers, or even the visual dimensions that make clothing items compatible. He has been an assistant professor at UC San Diego since 2014, and received his PhD from the Australian National University.

Hanghang Tong Hanghang Tong, Arizona State University
Title: Towards Optimal Teams in Big Networks
Abstract: In his world-widely renowned book “The Science of the Artificial”, Nobel laureate Herbert Simon pointed out that it is more the complexity of the environment, than the complexity of the individual persons, that determines the complex behavior of humans. The emergence of network science and the advent of big data, provides a new environment/context, where people interact and collaborate with each other to collectively perform some complex tasks. In this talk, we will summarize our recent effort on analyzing team performance in the context of big networks. First (characterization): we will present our findings on what kinds of network metrics/characteristics are crucial to team performance, and to what extent. Second (prediction): we will present predictive models to forecast the performance of a given team at an early stage, based on its network structure. Finally (enhancement): we will present tools and algorithms to enhance the performance of an existing team.
Bio: Hanghang Tong is currently an assistant professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. Before that, he was an assistant professor at Computer Science Department, City College, City University of New York, a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degree from Carnegie Mellon University in 2008 and 2009, both majored in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including one ‘test of time’ award (ICDM 10-Year highest impact paper award), four best paper awards and four ‘best of conference’. He has published over 100 referred articles and more than 20 patents.


Organizing Committee

Deguang Kong Deguang Kong Email is currently a staff research scientist at Samsung Research America, Silicon Valley, leading the design and implementation of large-scale security aware app recommendation and privacy risk ranking systems for mobile devices. Before joining Samsung Research America, he ever worked as a research intern at America Los Alamos National Lab and NEC Research Lab Silicon Valley. He has strong interdisciplinary background in machine learning/data mining and cyber security. He has worked on various research projects, including robust feature learning via structural sparsity, distance learning and label propagation for malware classification, security-aware mobile app ranking and recommendation, etc. He has published over 20 referred articles in top conferences, including KDD, ICDM, SDM, WSDM, CIKM, ICML, NIPS, AAAI, ECML/PKDD, CVPR, SIGMETRICS, INFOCOM, etc. He was invited to serve as a reviewer for numerous top conferences and journals, such as KDD, ICDM, CCS, INFOCOM, TKDE, TNNLS, TKDD, and DMKD, etc.

Jiayu Zhou Jiayu Zhou Email is currently an Assistant Professor at the Department of Computer Science and Engineering at the Michigan State University. Before joining MSU, he was a staff research scientist at Samsung Research America, leading the architecture design and development of a large-scale recommendation engine. Jiayu received his Ph.D. degree in computer science at Arizona State University in 2014. Jiayu has a broad research interest in large-scale machine learning and data mining, and biomedical informatics. He has served as Senior Program Committee of IJCAI 2015. He also served as program committee members in premier conferences such as NIPS, ICML, KDD, ICDM, SDM, WSDM, ACML and PAKDD. He serves as an Associate Editor of Neurocomputing. Most of Jiayu's research has been published in top machine learning and data mining venues including NIPS, SIGKDD, and ICDM. One of his papers has been selected for the best student paper award in ICDM 2014.

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

Publicity Chair

Shiyu Chang, University of Illinois at Urbana-Champaign

Website Chair

Zhangyang Wang, University of Illinois at Urbana-Champaign

Program Committee


Saturday, May 7. Afternoon Session

1:30p - 2:30 Workshop opening
Invited talk I: Use of Social Networks in Recommendation Systems
Speaker: Tina Eliassi-Rad, Rutgers University
2:30p - 3:30 Invited talk II: Harnessing reviews to build richer models of opinions
Speaker: Julian McAuley, University of California, San Diego
3:30p - 4:30 Invited talk III: Towards Optimal Teams in Big Networks
Speaker: Hanghang Tong, Arizona State University
4:30 - 4:38 Contributed paper talk: A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
Speaker: TBA
4:39 - 4:47 Contributed paper talk: Semi-supervised Collaborative Ranking with Push at Top
Speaker: TBA
4:48 - 4:56 Contributed paper talk: Modeling Trust for Rating Prediction in Recommender Systems
Speaker: TBA
4:57 - 5:00 Contributed paper talk: Towards Automatic Ranking App Risks via Heterogenous Privacy Indicators
Speaker: Deguang Kong , Samsung Research America
Accepted Papers
ID Title and Authors
1 A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
Ammar Alanazi, King Abdulaziz City for Science and Technology
Michael Bain, The University of New South Wales
2 Semi-supervised Collaborative Ranking with Push at Top
Iman Barjasteh, Michigan State University
Rana Forsati, Michigan State University
Abdol-Hossein Esfahanian, Michigan State University
Hayder Radha, Michigan State University
3 Modeling Trust for Rating Prediction in Recommender Systems
Anahita Davoudi, University of Central Florida
Mainak Chatterjee, University of Central Florida
4 Towards Automatic Ranking App Risks via Heterogenous Privacy Indicators
Deguang Kong, Samsung Research America
Lei Cen, Purdue University
Hongxia Jin, Samsung Research America