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3rd International Workshop on Machine Learning Methods for Recommender Systems

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When :  2017-04-27

Where :  Houston, Texas, USA

Submission Deadline :  2017-02-01

Categories :   Machine Learning ,  Data Mining      

Untitled Document

3rd International Workshop on Machine Learning Methods for Recommender Systems(MLRec 2017)

April 27 - 29, 2017, Houston, Texas, USA

Call for Papers :

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 recognized.

Topics of Interest

    - 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

Important Dates

  • Paper Submission: February 1, 2017
  • Author Notification: February 10, 2017
  • Camera Ready Paper Due: February 13, 2017
  • User Name : jerish
    Posted 04-01-2017 on 10:25:45 AEDT


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