# Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

## TL;DR

This paper introduces a Bayesian nonparametric model to extract hierarchical task and subtask structures from search query logs, aiming to improve search personalization and user experience.

## Contribution

It presents a novel hierarchical task extraction method that captures task/subtask relationships, unlike previous flat-structure approaches, enhancing search system capabilities.

## Key findings

- The model effectively identifies task hierarchies from real query logs.
- Hierarchical representations improve query suggestion and personalization.
- Experimental results demonstrate the method's accuracy and efficiency.

## Abstract

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.