MLRec 2015

From the archive of mlrec.org

In conjunction with 15th SIAM International Conference on Data Mining (SDM 2015) May 2, 2015, Vancouver, British Columbia, Canada The proceeding of MLRec’15 is now available for download . This workshop 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, Janurary 12 Feb 2, 2015. The site has started to accept manuscrips.

Important Dates Paper Submission: February 2, 2015 Author Notification: February 10, 2015 Camera Ready Paper Due: February 16, 2015 Workshop: May 2, 2015 Slides from speakers are now available in the Program . Invited Speakers Chih-Jen Lin , National Taiwan University Title: MF (Matrix Factorization) and FM (Factorization Machines) for Recommender Systems Abstract: MF (Matrix Factorization) and FM (Factorization Machines) are both effective methods for Recommender Systems.

In the first part of this talk, we focus on MF that assumes the ratings from users to items are the only given information. We discuss our recent efforts in developing a parallel package LIBMF for shared-memory systems. Currently, stochastic gradient (SG) method is one of the most popular algorithms to solve the optimization problem for MF.

However, as a sequential approach, SG is difficult to be parallelized. We carefully reduce the cache-miss rate and address the load balance of threads to have an effective parallel SG implementation. Further, because the performance of SG highly depends on the setting of learning rates, we develop some adaptive approaches to achieve fast and stable convergence.

Experiments show that our implementation outperforms available parallel matrix factorization packages. In the second part of the talk, we consider situations where some user or item features are also available. FM (Factorization Machines) is a useful model in such situations.

Recently an extension of FM called FFM (Field-aware FM) has been shown to be very effective for CTR predictions in computational advertising. We discuss our group members’ winning approaches for two Kaggle competitions on CTR predictions.

Our work has been released in another package LIBFFM for public use. Bio: Chih-Jen Lin is currently a distinguished professor at the Department of Computer Science, National Taiwan University. He obtained his B.S. degree from National Taiwan University in 1993 and Ph.D. degree from University of Michigan in 1998. His major research areas include machine learning, data mining, and numerical optimization.

He is best known for his work on support vector machines (SVM) for data classification. His software LIBSVM is one of the most widely used and cited SVM packages.

For his research work he has received many awards, including the ACM KDD 2010 and ACM RecSys 2013 best paper awards. He is an IEEE fellow, a AAAI fellow, and an ACM distinguished scientist for his contribution to machine learning algorithms and software design. More information about him can be found here . George Karypis , University of Minnesota Title: TBA Abstract: TBA Bio: 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 —> Martin Ester , Simon Fraser University Title: Probabilistic Graphical Models for Recommendation in Social Media Abstract: In this talk, we will take a closer look at two key ingredients of social media, social networks and location-based services, and investigate how to model their properties for effective item and location recommendation.

In the social sciences, the effects of social influence (friends tend to become more similar to each other), and homophily or selection (people tend to befriend similar people) have been identified as drivers of the dynamics of social networks. In the first part of the talk we will discuss methods for exploiting these effects for improving the accuracy of item recommendation in social networks.

In the second main part of the talk, we will explore another key aspect of social media, namely location-based social networks (LBSN). A key effect in LBSN is geographical influence, which means that nearby locations have more similar features than far away locations. Location recommendation is different in nature from traditional item recommendation, since the check-in at a physical location requires more commitment from a user than the adoption of an item such as a movie. We will present probabilistic graphical models to capture these properties for location recommendation.

The talk will discuss related research with a focus on our own work. In the conclusion, we will outline interesting directions for future research in the fascinating field of recommendation in social media.

Bio: Martin Ester received a PhD in Computer Science from

MLRec 2015

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