Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamic hierarchical Dirichlet process
Proceedings of the 25th international conference on Machine learning
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Dynamic mixture models for multiple time series
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Topic tracking model for analyzing consumer purchase behavior
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Topic tracking language model for speech recognition
Computer Speech and Language
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A time-dependent topic model for multiple text streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining and forecasting of complex time-stamped events
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time helpfulness prediction based on voter opinions
Concurrency and Computation: Practice & Experience
Topic model for analyzing purchase data with price information
Data Mining and Knowledge Discovery
Towards Topic Trend Prediction on a Topic Evolution Model with Social Connection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Automatic tag recommendation for metadata annotation using probabilistic topic modeling
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 22nd international conference on World Wide Web
Dynamic joint sentiment-topic model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topic-specific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps.