This workshop aims to foster discussions in several fields that are of interest to our growing community of recommendation system builders. On the practical side, we would like to encourage sharing of architecture and algorithm best practices in large-scale recommender systems as they are practiced in industry, as well as particular challenges and pain points. We hope this will guide future research that is system aware. On the research side, we focus on bringing in ideas and evaluations on scaling beyond the current generation of big data systems, with improved recommendation metrics. We believe the brightest minds from both sides will mutually benefit from the discussions and accelerate problem solving.

We invite submissions in two formats: extended abstracts (1-8 pages), or slides (15-20 slides). By accepting slides, we hope to lower the writing burden for industry participants. However, since slides submissions sometimes are short on details, we might request clarification or additional editing as condition for acceptance. We encourage contributions in new theoretical research, practical solutions to particular aspects of scaling a recommender, best practices in scaling evaluation systems, and creative new applications of big data to large scale recommendation systems.

Tao Ye, Pandora Inc.
Denis Parra, PUC Chile
Vito Ostuni, Pandora Inc.
Tao Wang, Apple Inc.