Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
MultiMediaMiner: a system prototype for multimedia data mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Fast and low-cost search schemes by exploiting localities in P2P networks
Journal of Parallel and Distributed Computing
Vizster: Visualizing Online Social Networks
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
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Social network contents are not limited to text but also multimedia. Dailymotion, YouTube, and MySpace are examples of successful sites which allow users to share videos among themselves. Due to the huge amount of videos, grouping videos with similar contents together can help users to search videos more efficiently. Unlike the traditional approach to group videos into some predefined categories, we propose a novel comment-based matrix factorization technique to categorize videos and generate concept words to facilitate searching and indexing. Since the categorization is learnt from users feedback, it can accurately represent the user sentiment on the videos. Experiments conducted by using empirical data collected from YouTube shows the effectiveness of our proposed methodologies.