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

TL;DR

This paper introduces the Subtopic Aware Complex Searcher Model (SACSM), an extension of the Complex Searcher Model, to better simulate user behaviors in complex Search as Learning tasks by modeling subtopics as aspects of information needs.

Contribution

The paper develops SACSM, a novel model that incorporates subtopics into searcher behavior modeling, enabling more accurate simulation of complex search tasks in the SAL domain.

Findings

SACSM accurately simulates user behaviors under certain conditions.

Different subtopic selection strategies influence simulated user behavior.

Large-scale simulations provide insights into SAL user interactions.

Abstract

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.