Searching, Learning, and Subtopic Ordering: A Simulation-Based Analysis

Arthur Câmara*, David Maxwell, Claudia Hauff

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


Complex search tasks—such as those from the Search as Learning (SAL) domain—often result in users developing an information need composed of several aspects. However, current models of searcher behaviour assume that individuals have an atomic need, regardless of the task. While these models generally work well for simpler informational needs, we argue that searcher models need to be developed further to allow for the decomposition of a complex search task into multiple aspects. As no searcher model yet exists that considers both aspects and the SAL domain, we propose, by augmenting the Complex Searcher Model (CSM), the Subtopic Aware Complex Searcher Model (SACSM)—modelling aspects as subtopics to the user’s need. We then instantiate several agents (i.e., simulated users), with different subtopic selection strategies, which can be considered as different prototypical learning strategies (e.g., should I deeply examine one subtopic at a time, or shallowly cover several subtopics?). Finally, we report on the first large-scale simulated analysis of user behaviours in the SAL domain. Results demonstrate that the SACSM, under certain conditions, simulates user behaviours accurately.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Proceedings
EditorsMatthias Hagen, Suzan Verberne, Craig Macdonald, Christin Seifert, Krisztian Balog, Kjetil Nørvåg, Vinay Setty
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030997359
Publication statusPublished - 2022
Event44th European Conference on Information Retrieval, ECIR 2022 - Stavanger, Norway
Duration: 10 Apr 202214 Apr 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13185 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference44th European Conference on Information Retrieval, ECIR 2022


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