Keynote: Content, Connections, and Context (Daniel Tunkelang)

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.

Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval (HCIR). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.