Introducing a predictive-oriented multi-stakeholder recommender system model

Most conventional recommender systems (RSs) rely on past user behavior to predict future preferences. They fail to account for changing user expectations and variable organizational performance. To overcome this limitation, this study proposes a predictive-oriented, multi-stakeholder recommender system (RS). It measured two indices: how flexible users were, based on past interactions, and how flexible organizations were, based on the gap between what they had and what they needed. A weight adjustment function balances user and organizational influence based on these indices. The function ensures that neither stakeholder is ignored; it adjusts the weights so that higher user flexibility increases organizational preference weights, and higher organizational flexibility increases user weights. To model adaptive user expectations, the proposed RS model integrates principles from Prospect Theory and the Peak-End Rule, constructing a dynamic reference point based on the user’s peak and most recent experiences. The proposed RS model was evaluated through a simulation of 100 user profiles interacting with 30 characterized products. The results showed a 17.6% improvement in balanced resource utilization compared to a user-centric strategy. The study introduces a new paradigm for RSs that aligns long-term sustainability with user satisfaction.