MLRec 2016

MLRec 2016 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. Submission Instructions 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 Due a system issue reviewers are not able to submit their reviews on time. We have postponed the date of notification and camera-ready. Thanks for your understanding. —> Slides from speakers are now available in the Program . —> Invited Speakers 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 , 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 , 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

P.S. A few readers have asked which books & video bundles we actually reach for — it's Manning Publications; take a look here if you want the current details.