Extracting Search Tasks from Query Logs Using a Recurrent Deep Clustering Architecture

Users fulfill their information needs by expressing them using search queries and running the queries in available search engines. The mining of query logs from search engines enables the automatic extraction of search tasks by clustering related queries into groups representing search tasks. The extraction of search tasks is crucial for multiple user supporting applications like query recommendation, query term prediction, and results ranking depending on search tasks. Most existing search task extraction methods use graph-based or nonparametric models, which grow as the query log size increases. Deep clustering methods offer a parametric alternative, but most deep clustering architectures fail to exploit recurrent neural networks for learning text data representations. We propose a recurrent deep clustering model for extracting search tasks from query logs. The proposed architecture leverages self-training and dual recurrent encoders for learning suitable latent representations of user queries, outperforming previous deep clustering methods. It is also a parametric approach that offers the possibility of having a fixed-sized architecture for analyzing increasingly large search query logs.