TY - GEN
T1 - Searching, Learning, and Subtopic Ordering
T2 - 44th European Conference on Information Retrieval, ECIR 2022
AU - Câmara, Arthur
AU - Maxwell, David
AU - Hauff, Claudia
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85128716477&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99736-6_10
DO - 10.1007/978-3-030-99736-6_10
M3 - Conference contribution
AN - SCOPUS:85128716477
SN - 9783030997359
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 142
EP - 156
BT - Advances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Proceedings
A2 - Hagen, Matthias
A2 - Verberne, Suzan
A2 - Macdonald, Craig
A2 - Seifert, Christin
A2 - Balog, Krisztian
A2 - Nørvåg, Kjetil
A2 - Setty, Vinay
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 10 April 2022 through 14 April 2022
ER -