A Deep‐Learning Approach for Three‐Dimensional Confirmation Prediction in Data Retrieval

ABSTRACT Expectation confirmation in data retrieval systems remains a critical yet understudied issue. To bridge this gap, this paper proposes a multi‐dimensional framework for investigating the confirmation formation in the context of data retrieval, drawing upon the Expectation‐Confirmation Theory and Expectancy‐Value Theory. Furthermore, it introduces a deep‐learning model called Multiple‐Expert System based on Bayesian Neural Networks (MES‐BNN) to predict multi‐dimensional confirmation by analyzing users’ search behavior data. The findings reveal the multi‐dimensional and context‐dependent nature of confirmation. In the data retrieval context, users discern gaps between their search experiences and expectations in terms of task, cognition, and emotion, collectively forming a three‐dimensional confirmation. This three‐dimensional confirmation can be predicted through search behavior data mining. Furthermore, the MES‐BNN model demonstrates its effectiveness in mining small‐scale behavioral data, enabling automatic and accurate prediction of the three‐dimensional confirmation and contributes to advancing data analytical approaches employed in user‐oriented retrieval studies in the intelligent age.