Taking Search to Task

TL;DR

This paper reviews the evolution of task-based approaches in information retrieval, highlighting challenges and proposing a new tree-like framework to better understand and address user tasks, especially in conversational and proactive IR contexts.

Contribution

It offers a comprehensive synthesis of past and current perspectives on task-focused IR and introduces a novel tree-structured framing device for understanding user tasks.

Findings

Current IR often overlooks user tasks beyond query collection.

A new tree-like model helps interpret and utilize task information more effectively.

Identifies technical, social, and ethical challenges in task-based IR.

Abstract

The importance of tasks in information retrieval (IR) has been long argued for, addressed in different ways, often ignored, and frequently revisited. For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist. But for the most part, the IR community has been too focused on query processing and assuming a search task to be a collection of user queries, often ignoring if or how such an assumption addresses the users accomplishing their tasks. With emerging areas of conversational agents and proactive IR, understanding and addressing users' tasks has become more important than ever before. In this paper, we provide various perspectives on where the state-of-the-art is with regard to tasks in IR, what are some of the bottlenecks in deriving and using task information, and how do we go forward from here. In addition to covering relevant literature, the paper provides a synthesis of historical and current perspectives on understanding, extracting, and addressing task-focused search. To ground ongoing and future research in this area, we present a new framing device for tasks using a tree-like structure and various moves on that structure that allow different interpretations and applications. Presented as a combination of synthesis of ideas and past works, proposals for future research, and our perspectives on technical, social, and ethical considerations, this paper is meant to help revitalize the interest and future work in task-based IR.