A Probabilistic Model Toward How People Search to Build Outcomes - Yuli Zhao; Yuyang Bai; Yin Zhang; Bin Zhang; Pertti Vakkari (IEEE Access, 2023)
Although increased attention is being given to understanding how people search to build task outcomes, a formal model of the relation between how people search and how people build task outcomes is still lacking. This paper proposes a unified probabilistic model of how people search to build outcomes. The model involves 3 types of searcher behaviors (i.e., query submission, document selection, and information transformation) to model the effect of the information collected during search, and uses the item response theory to capture the ternary relations between the ability to transform information, the information collected, and the probability of successfully building task outcomes. We evaluate the proposed model in the task of identifying searchers’ proficiencies under the assumption that high proficiency searchers would have high abilities to transform information. The results obtained high accuracies and F1 scores, which could reflect the effectiveness of the proposed model. The model contributes to the formal understanding of how people search to build task outcomes, and provides new possibilities for personalized and session-based information retrieval research.