Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
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
Linked Topic and Interest Model for Web Forums
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
ATTention: understanding authors and topics in context of temporal evolution
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Understanding Topical trends and user roles in topic evolution is an important challenge in the field of information retrieval. In this contribution, we present a novel model for analyzing evolution of user's interests with respect to produced content over time. Our approach Author-Topic-Time model (ATT) addresses this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We extend state of the art Latent Dirichlet Allocation (LDA) topic model to incorporate the author and timestamp information for capturing changes in user interest over time with respect to evolving latent topics. We present results of application of the model to the 9 years of scientific publication datasets from CiteSeer showing improved semantically cohesive topic detection and capturing shift in authors interest in relation to topic evolution. We also discuss opportunities of model use in novel mining and recommendation scenarios.