In the 6th ACM International Conference on Recommender Systems (RecSys 2012)
The pervasiveness of social networks has magnified the utility of recommender systems and all three classical dimensions users, items and modes of interactions i.e. click or buy etc. have exploded in scale: more users, more heterogeneous items, and diverse interactions.
In this talk we present the challenges and opportunities of applying simple to sophisticated machine learning, data mining, and statistical modeling techniques to the world of recommender problems in social networks. Using real world example applications deployed on LinkedIn, we build from foundational literature on content based recommendations, collaborative filtering, and behavioral targeting techniques to arrive at the formalism of Social Filtering. We then cover critical aspects of developing of a web scale social recommender systems including infrastructure, feature engineering and model fitting. We describe some of the most fascinating challenges faced in the real-world setting of operating recommender systems including scalability, offline vs online tradeoffs, A/B Testing, and Multiple Objective Optimization. Finally, conclude with some new and unique paradigms of virtual profiling, social referral and intent-interest modeling, in the context of the LinkedIn recommender system.