Referral Web: combining social networks and collaborative filtering
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommender Systems Research: A Connection-Centric Survey
Journal of Intelligent Information Systems
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A review of probabilistic macroscopic models for swarm robotic systems
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A collaborative approach to build evaluated web page datasets
Future Generation Computer Systems
Information Systems Frontiers
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The rise of social media sites, such as blogs, wikis, Digg and Flickr among others, underscores a transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing information. The social news aggregator Digg allows users to submit links to and vote on news stories. Like other social media sites, Digg also allows users to designate others as "friends" and easily track friends' activities: what new stories they submitted, commented on or liked. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. We construct a mathematical model to study how collaborative rating and promotion of news stories emerges from independent decisions made by many users. The model takes into account user behavior: e.g., whether they read stories on the front page or through the Friends interface. Solutions of the model qualitatively reproduce the observed dynamics of votes received by actual stories on Digg. Digg also ranks users according to how successful they are in getting their stories promoted to the front page. We create a model that describes how a user's rank changes in time as he gets more stories to the front page and becomes more influential in the community. We find qualitative agreement between predictions of the model and the evolution of rank for Digg users. The Digg model of allowing users to collectively evaluate how interesting the news stories are can be generalized to collaborative evaluation of the quality of information. Mathematical analysis can be used as a tool to explore different voting methods to select the most effective one before the method is ever implemented in a real system.