The Journal of Machine Learning Research
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
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
Mining geographic knowledge using location aware topic model
Proceedings of the 4th ACM workshop on Geographical information retrieval
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
GeoFolk: latent spatial semantics in web 2.0 social media
Proceedings of the third ACM international conference on Web search and data mining
Equip tourists with knowledge mined from travelogues
Proceedings of the 19th international conference on World wide web
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Context-aware search personalization with concept preference
Proceedings of the 20th ACM international conference on Information and knowledge management
LPTA: A Probabilistic Model for Latent Periodic Topic Analysis
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
G-WSTD: a framework for geographic web search topic discovery
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to rank for spatiotemporal search
Proceedings of the sixth ACM international conference on Web search and data mining
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Mining the latent topics from web search data and capturing their spatiotemporal patterns have many applications in information retrieval. As web search is heavily influenced by the spatial and temporal factors, the latent topics usually demonstrate a variety of spatiotemporal patterns. In the face of the diversity of these patterns, existing models are increasingly ineffective, since they capture only one dimension of the spatiotemporal patterns (either the spatial or temporal dimension) or simply assume that there exists only one kind of spatiotemporal patterns. Such oversimplification risks distorting the latent data structure and hindering the downstream usage of the discovered topics. In this paper, we introduce the Spatiotemporal Search Topic Model (SSTM) to discover the latent topics from web search data with capturing their diverse spatiotemporal patterns simultaneously. The SSTM can flexibly support diverse spatiotemporal patterns and seamlessly integrate the unique features in web search such as query words, URLs, timestamps and search sessions. The SSTM is demonstrated as an effective exploratory tool for large-scale web search data and it performs superiorly in quantitative comparisons to several state-of-the-art topic models.