Key lessons learned building recommender systems for large-scale social networks

By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems. This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virality preserving online testing and virtual profiling.