RWTH Aachen University
Many web properties make extensive use of item-based collaborative filtering, which showcases relationships between pairs of items based on the wisdom of the crowd. This paper presents LinkedIn's horizontal collaborative filtering infrastructure, known as browsemaps. The platform enables rapid development, deployment and computation of collaborative filtering recommendations for almost any use case on LinkedIn. In addition, it provides centralized management of scaling, monitoring, and other operational tasks for online serving. The authors also present case studies on how LinkedIn uses this platform in various recommendation products, as well as lessons learned in the field over the several years this system has been in production.