Taking Search to Task
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Jan 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomiescited by 34
- 2023Advancing the Search Frontier with AI Agentscited by 12
- 2024From Prompt Engineering to Prompt Science With Human in the Loopcited by 12
- 2026Analytical Searchcited by 1
- 2026Analytical Search12 Feb 2026
- 2024From Prompt Engineering to Prompt Science With Human in the Loop1 Jan 2024
- 2023Advancing the Search Frontier with AI Agents2 Nov 2023
- 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies14 Sep 2023