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

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.