Mining web search topics with diverse spatiotemporal patterns

Di Jiang, Wilfred Ng

Abstract: 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.