Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Proceedings of the 6th international conference on Intelligent user interfaces
A vector space model for automatic indexing
Communications of the ACM
A face(book) in the crowd: social Searching vs. social browsing
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visualizing personal relations in online communities
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
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Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends' social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends' updates and other activities). To address these two problems, we propose a user-centric system called ''SocConnect'' (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends' activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends' activities and help users deal with cognitive overload.