Do your 500 "friends" on social networks really know what you will like? How many of your friends' shared links that you click each day are interesting to you? The social graph brings trust and meaning to the web, but often creates information overload from over-sharing. And because real-time updates and feeds emphasize recency over relevance, rare gems often fall through the cracks. This talk will discuss the issues and considerations when designing a personalized discovery engine, one that combines the social, peer and taste graphs to produce relevant, peer-sourced recommendations and serendipitous discovery of new online content. StumbleUpon CEO Garrett Camp will go over the concepts and mechanisms behind such recommendation systems, and highlight findings from analysis of StumbleUpon's database of over 15 billion personalized stumbles.
Garrett has guided StumbleUpon’s development since 2001, from inception to over 15M registered members. StumbleUpon is now the largest personalized content discovery engine on the web, delivering over 800 million personalized recommendations per month from its index of 60 million human-submitted pages. Garrett completed his Masters in Software Engineering at the University of Calgary, where he researched interfaces for collaborative systems, evolutionary algorithms and information retrieval. He is also a 2007 recipient of MIT Technology Review’s TR35 award.
I'm the CTO and EVP of Product at Pandora.
