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

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活动简介

Following the success of the last editions of MLRec in 2015 and 2016, 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.

征稿信息

重要日期

2017-02-01

初稿截稿日期

2017-02-10

初稿录用日期

2017-02-13

终稿截稿日期

征稿范围

Novel machine learning algorithms for recommender systems, e.g., new content-based or context-aware recommendation algorithms, new algorithms for matrix factorization, tensor-based approaches for recommender systems, etc.
Novel applications of existing machine learning and data mining algorithms for recommender systems, e.g., applying bilinear models, (non-convex) sparse learning, metric learning, low-rank approximation/PCA/SVD, neural networks and deep learning, etc.
Novel optimization techniques for improving recommender systems, e.g., parallel/distributed optimization techniques, efficient stochastic gradient descent, etc.
Industrial practices and implementations of recommendation systems, e.g., feature engineering, model ensemble, large-scale implementations of recommender systems, etc.
Emerging recommendation problems and scenarios in industry and their ML-based solutions, e.g., recommendation for e-fashion, etc.
Novel recommendation problems in non-conventional recommender system research areas (e.g., precision medicine, health informatics) and their ML-based solutions, e.g., recommendation of physicians, recommendation of healthy life-styles for seniors, etc.