Project COST – Modelling Complex Search tasks
Search engines, and more generally search systems, are the main access to a world-scale digital library, allowing people to achieve search tasks. In the CoST project, we envision a shift from search engines to task completion engines by dynamically assisting users in making the optimal decisions, empowering them to achieve multi-step and highly cognitive search tasks. This triggers the need for (1) more predictable and automatic models of user-system interaction and search tasks and, (2) more task-oriented information access models.
Context
In the past years, the range and level of complexity of search tasks significantly increased from simple ones like fact-finding to more intensive knowledge-oriented tasks like hypothesis-directed search for medical diagnosis or human learning for educational purposes. Such tasks span multiple sessions, require sustained user-system interaction, engagement with information, and are structured in multiple subtasks and/or multiple topics. While search systems today are very efficient for simple look-up information tasks (fact-finding search), they are unable to guide users engaged in complex search processes. Hence, paradoxically, while we consider information search nowadays to be ‘natural’ and ‘easy’, search systems are not yet able to provide adequate support for achieving a wide range of real-life work search tasks.
Objectives
- Model patterns of search behavior from user interactions. The goal is to mine high-level user behavior patterns by jointly relating multiple observable user interactions (e.g., query reformulation, clicks) to both subtasks and task attributes (e.g., level of cognitive complexity) and user’s cognitive context (e.g., domain knowledge).
- Learning representations of complex search tasks. By analogy with the importance of query and document representation in traditional IR models, this step is fundamental for designing task-based information access models. In CoST, we attempt to build the representations of tasks that support their completion based on system-driven assistance.
- Designing task-driven information access models. We consider here the problem of matching information relevance with task completion. Only a very few and recent work tackled this challenge in the context of specific tasks. Our aim in the CoST project is to provide solutions to generic complex search tasks by relying on their learned representations and understanding of cognitive users’ search abilities.
Results
The expected results for the project are:
- A dataset with user logs (generated during complex search tasks, in French).
- Publications in major IR conferences and journals
Partnerships and collaborations
- ISIR. Objective: Models for users engaged in complex search tasks.
- Laboratoire Cognition, Langues, Langage, Ergonomie de Toulouse 2 (CLLE). Objective: Identify cognitive processes and strategies developed by end-users during cognitive human learning tasks in order to enhance cognitive information search models used during the first stage of retrieval.
- Laboratoire d’Informatique de Grenoble (LIG). Objective: Model the task-based retrieval search.
- Institut de Recherche en Informatique de Toulouse (IRIT). Objective: Model search tasks and subtasks representations, and Identify related patterns of users’ behaviors in search sessions.
Project: https://www.irit.fr/COST/
