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MLRec 2015 : 1st International Workshop on Machine Learning Methods for Recommender Systems


When Apr 30, 2015 - May 2, 2015
Where British Columbia, Canada
Submission Deadline Jan 12, 2015
Notification Due Jan 30, 2015
Final Version Due Feb 9, 2015
Categories    data mining   machine learning   recommendation system

Call For Papers

* Apologies if you received multiple copies of this CFP.
* Please kindly forward to those who may be interested.

Call for Papers

1st International Workshop on Machine Learning Methods for Recommender Systems

To be held in conjunction with 15th SIAM International Conference on Data Mining (SDM 2015)

May 2, 2015
Vancouver, BC, Canada

Important dates
Paper Submission: January 12, 2015
Notification of Acceptance: January 30, 2015
Camera Ready Paper Due: February 9, 2015

This workshop aims to bring the attention of researchers to the various data mining and machine learning methods for recommender systems. Since the introduction of recommender system, there are a lot of machine learning and data mining algorithms designed for effective and efficient recommendation. To name a few, the matrix factorization techniques are widely used to model the latent space in which users and items interact with each other. The factorization machine uses bilinear regression models to capture the non-linear interactions among the user features and item features. In the past years, researchers have utilized many machine learning techniques such as online learning, metric learning, sparse learning, multi-task learning, reinforcement learning to foster the development of recommender systems.

This workshop focuses on applying novel as well as existing machine learning and data mining methodologies for improving recommender systems. Indeed 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.

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 recommendation systems, 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 learned 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 and multi-armed bandits for recommendation, etc

Paper submission and reviewing will be handled electronically. Authors should consult the workshop Web site for full details regarding paper preparation and submission guidelines at

George Karypis, University of Minnesota
Jiayu Zhou, Samsung Research America
Deguang Kong, Samsung Research America

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