GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling distances in large-scale networks by matrix factorization
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Social Computing and Weighting to Identify Member Roles in Online Communities
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Increasing participation in online communities: A framework for human-computer interaction
Computers in Human Behavior
Exploring Local Community Structures in Large Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Who will be participating next?: predicting the participation of Dark Web community
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
User Interest and Topic Detection for Personalized Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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This paper studies the online discussion participation prediction (OFPP). Online discussion is an application on the web that provides a cyberspace for users to exchange or share different information. Finding suitable online discussions on Internet becomes difficult as huge amount of information existed. This led to recommendation systems that provide advices to users. In this paper, a weighted non-negative matrix factorization method is used to discover latent user preferences of online discussions such that prediction of user's participation can be obtained. Experimental results show that with the prediction of user's preferences, suitable online discussions can be suggested to the user.