Tag recommendations in social bookmarking systems
AI Communications
Detecting health events on the social web to enable epidemic intelligence
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Towards personalized learning to rank for epidemic intelligence based on social media streams
Proceedings of the 21st international conference companion on World Wide Web
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Taking the Pulse of Political Emotions in Latin America Based on Social Web Streams
LA-WEB '12 Proceedings of the 2012 Eighth Latin American Web Congress
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The collective effervescence of social media production has been enjoying a great deal of success in recent years. The hundred of millions of users who are actively participating in the Social Web are exposed to ever-growing amounts of sites, relationships, and information. In this paper, we report part of the efforts towards the realization of a Web Observatory at the L3S Research Center (www.L3S.de). In particular, we present our approach based on Living Analytics methods, whose main goal is to capture people interactions in real-time and to analyze multidimensional relationships, metadata, and other data becoming ubiquitous in the social web, in order to discover the most relevant and attractive information to support observation, understanding and analysis of the Web. We center the discussion on two areas: (i) Recommender Systems for Big Fast Data and (ii) Collective Intelligence, both key components towards an analytics toolbox for our Web Observatory.