Lili Wu, Sam Shah, Sean Choi, Mitul Tiwari and Christian Posse

In the 6th ACM RecSys Workshop on Recommender Systems & the Social Web (RSWeb 2014)



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 \emph{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. We 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.