Towards Hierarchies of Search Tasks & Subtasks

Current search systems do not provide adequate support for users tackling complex tasks due to which the cognitive burden of keeping track of such tasks is placed on the searcher. As opposed to recent approaches to search task extraction, a more naturalistic viewpoint would involve viewing query logs as hierarchies of tasks with each search task being decomposed into more focussed sub-tasks. In this work, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks & subtasks. The proposed approach makes use of the multi-relational aspect of query associations which are important in identifying query-task associations. We describe a greedy agglomerative model selection algorithm based on the Gamma-Poisson conjugate mixture that take just one pass through the data to learn a fully probabilistic, hierarchical model of trees that is capable of learning trees with arbitrary branching structures as opposed to the more common binary structured trees. We evaluate our method based on real world query log data based on query term prediction. To the best of our knowledge, this work is the first to consider hierarchies of search tasks and subtasks.