This is keynote presentation I delivered at the Workshop on Recommender Systems and the Social Web, held as part of the 6th ACM International Conference on Recommender Systems (RecSys 2012):
Content, Connections, and Context
Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context.
Content comes first – we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate.
I’ll talk about how we use these three kinds of signals in LinkedIn’s recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.
When I’m back from Dublin, I promise to blog about my impressions and reflections from the conference. In the mean time, I hope you enjoy the slides!