Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
TCRec: product recommendation via exploiting social-trust network and product category information
Proceedings of the 22nd international conference on World Wide Web companion
User-defined hot topic detection in microblogging
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
| Hi-index | 0.00 |
With the overwhelming information from social media networks and news portals, it is crucial to provide users a complete package of visual and textual information with popular interests automatically. To this concern, we present a news detection and pushing system, called Me-Digger (Multimedia News Digger), which not only effectively detects emerging topics from social streams but also provides the corresponding information in multiple modalities. Me-digger is the first systematic effort to leverage three sources of data, that is, Twitter, Flickr and Google news, to output with vivid visual and textual contents on emerging topics. Enabled by a novel general-structured high-order co-clustering approach, it has a more accurate detection of emerging topics compared to the existing methods on micro-blog social streams.