Referral Web: combining social networks and collaborative filtering
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
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
Social Information Processing in News Aggregation
IEEE Internet Computing
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
Quality of content in web 2.0 applications
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Discerning actuality in backstage: comprehensible contextual aging
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Robust multivariate autoregression for anomaly detection in dynamic product ratings
Proceedings of the 23rd international conference on World wide web
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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 two mathematical models of collaborative decision-making on Digg. First, we study how collective rating of news stories emerges from the decisions made by many users. The model takes into account the effect that decisions made by a user's friends have on the user. We also study how user's influence, as measured by her rank, changes in time as she submits new stories and is befriended by other Digg users. Solutions of both models reproduce the observed dynamics of voting and user rank on Digg. The Digg model that enables users to collectively rate news stories can be generalized to the collaborative evaluation of document (or information) quality. Mathematical analysis can be used as a tool to explore different collaborative decision-making algorithms to select the most effective one before the algorithm is ever implemented in a real system.