Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge Engineering
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Event detection from evolution of click-through data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Euclidean Embedding of Co-occurrence Data
The Journal of Machine Learning Research
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Accounting for burstiness in topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Independent factor topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structured correspondence topic models for mining captioned figures in biological literature
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Explaining instance classifications with interactions of subsets of feature values
Data & Knowledge Engineering
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Topic Distributions over Links on Web
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Semi-supervised semantic role labeling using the latent words language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Probabilistic Topic Models for Learning Terminological Ontologies
IEEE Transactions on Knowledge and Data Engineering
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Reinforcement learning based resource allocation in business process management
Data & Knowledge Engineering
| Hi-index | 0.00 |
Statistical topic models have been proposed for modeling documents and authorship information. However, few previous works have studied the evolution of associated data. In this paper, we investigate how to model trends of changes in document content and author interests simultaneously over time. We propose two models: a bag-of-words based Author-Time-Topic model that extends the state-of-the-art LDA-style topic model and a Hidden Markov Author-Time-Topic model, which can model interdependencies between topics. We use the Gibbs EM algorithm for parameter estimation. We apply these models to two data sets: NIPS papers and Yahoo group posts. Experimental results show that our models can achieve a lower perplexity (-2.0%-20%) than the baseline LDA and Author-Topic model, when modeling quickly evolving associated data. Experiments also reveal that the proposed models can accurately capture the hot topics in different periods (e.g. ''Yao at preseason'' in Aug-2004, when the Chinese player Ming Yao became a highlight in the NBA) from the two data sets.