Overview

While search systems today are very efficient for simple look-up information tasks (fact-finding search), they are unable to guide users engaged in exploratory, multi-step and highly cognitive search tasks (e.g, diagnosis, human learning). 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 complex search tasks[1]. In the CoST project (funded by ANR 2019-2022), 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 complex search tasks. While most of previous work rely on query-aware models and techniques to structure the session context and model search satisfaction [2,3,4] at the query level, we rather attempt to design task-aware IR models to make task-level satisfaction predictions.
This internship will be focused on: (1) a review of recent neural approaches for next query prediction in session-based search; (2) the development of a baseline framework for query prediction in task-based search.

– The successful candidate is expected to have skills/background in information retrieval and machine learning.

Company:

Université Paul Sabatier

Specific requirements:

– Starting and duration: March 2019, 4-6 months

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