Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Footprints: history-rich tools for information foraging
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Capturing human intelligence in the net
Communications of the ACM
ScentTrails: Integrating browsing and searching on the Web
ACM Transactions on Computer-Human Interaction (TOCHI)
SERF: integrating human recommendations with search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
User Modeling and User-Adapted Interaction
ASSIST: adaptive social support for information space traversal
Proceedings of the eighteenth conference on Hypertext and hypermedia
A live-user evaluation of collaborative web search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The other side of the social web: a taxonomy for social information access
Proceedings of the 18th Brazilian symposium on Multimedia and the web
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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.