Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences

Abstract

Outbrain is the Web's leading content discovery service, recommending billions of stories daily to a global audience across many of the world's most prestigious and respected publishers. Outbrain's recommendation technology com- bines contextual cues with personalization, where the per- sonalization aspects are a combination of content-based and collaborative filtering techniques. This paper, and the accompanying demo, offer a behind- the-scenes view of the content-based aspects of Outbrain's personalization technology. We detail the types of features we extract from content, as well as the attributes we keep in each user's content-affinity profile. We then describe and demonstrate how we update each user's profile, in real time, as the user consumes content while browsing the Web.