Established in 2005, YouTube is one of the fastest-growing websites, and has become one of the most accessed sites in the Internet. It has a significant impact on the Internet traffic distribution, but itself is suffering from severe scalabilityconstraints. Understanding the features of YouTube and similar video sharing sites is thus crucial to network traffic engineeringand to sustainable development of this new generation of services.

The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.