Workshop on Large Scale Recommendation Systems

Modern Recommender Systems face greatly increased data volume and complexities. Previous computational models and experience on small data may not hold today, thus, how to build an efficient and robust system has become an important issue for many practitioners. 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 systems in order to: (1) share experience, techniques and methodologies used to develop effective large-scale recommenders, from architecture, algorithms, programming models, to evaluation (2) challenge conventional wisdom (3) identify key challenges and promising trends in the area, and (4) identify collaboration opportunities among participants.

  • Tao Ye, Pandora, USA
  • Danny Bickson, Dato, USA
  • Denis Parra, PUC Chile, Chile


Friday, Sept 16, 2016, 09:00-17:30


IBM (Aud B)

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