Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Vizster: Visualizing Online Social Networks
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
A survey of trust and reputation systems for online service provision
Decision Support Systems
Methodologies and Algorithms for Group-Rankings Decision
Management Science
Algorithms and incentives for robust ranking
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Axiomatic foundations for ranking systems
Journal of Artificial Intelligence Research
An axiomatic approach to personalized ranking systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Opinion space: a scalable tool for browsing online comments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The diversity donut: enabling participant control over the diversity of recommended responses
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Adaptive visualization for exploratory information retrieval
Information Processing and Management: an International Journal
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This extended abstract presents a new spatial model for collaboratively recommending "compelling" comments in an online discussion forum that promote consensus among a diverse group of users. In this application our goal is to promote comments that are rated highly by dissimilar users, which in some sense is a dual to traditional recommender problems. We propose a model for weighting and aggregating comment ratings that gives greater influence to positive ratings from users who tend to disagree with the commenter, and we compare it with various alternate methods. The model has the added benefit of being resistant to manipulation by false ratings and sybil attacks. We test the model on comments in Opinion Space, a new online discussion tool that allows users to visualize where they stand with respect to other users in terms of their opinions on a set of controversial propositions. Comments in the system are recommended visually, where more "compelling" comments are emphasized with larger sizes.