This will be the 5th installment of a mini-conference style workshop that focuses on practical and scaling issues for recommender systems. Modern recommender systems face greatly increased data volume and complexities. Computational models and experience on small data may not hold for millions of users, thus, how to build an efficient and robust system has become an important issue for many practitioners. Even well known models might have different performance on different domains’ data. Meanwhile, there is an increasing gap between academia research of recommendation systems focusing on complex models, and industry practice focusing on solving problems at large scale using relatively simple techniques. Evaluation of models have diverged as well. While most publications focus on fixed datasets and offline ranking measures, industry practitioners tend to use long term engagement metrics to make final judgments. The motivation of this workshop is to bring together researchers and practitioners working on large-scale recommender system in order to: (1) share experience, techniques and methodologies used to develop effective large-scale recommender, from architecture, algorithms, programming model, to evaluation (2) challenge conventional wisdom (3) identify key challenges and promising trends in the area, and (4) identify collaboration opportunities among participants.