Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
Enterprise expert and knowledge discovery
Proceedings of the HCI International '99 (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Communication, Cooperation, and Application Design-Volume 2 - Volume 2
The Journal of Machine Learning Research
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topic evolution and social interactions: how authors effect research
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The ContactFinder agent: answering bulletin board questions with referrals
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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In recent years a number of graphical models have been proposed for Topic discovery in various contexts and network analysis. However there is one class of document corpus, documents with ratings, where the problem of topic discovery has not been explored in much detail. In such document corpuses reviews and ratings of documents in addition to the documents themselves are also available. In this paper we address the problem of discovery of latent structures in document-review corpus which can then be used to construct a social network of experts. We present a graphical model COLBERT that automatically discovers latent topics based on the contents of the document, the review of the document and the ratings of the review.