Personalized Decision Support Enhanced by Multiple Expected Solution Points in the Characteristic Objects Method

This paper introduces a novel decision-making framework designed to enhance personalized support in Decision Support Systems (DSS). Existing methods often rely on single-dimensional modeling, which can introduce bias and uncertainty in criteria weighting. To address this, the study introduces a new approach for integrating subjective preferences with the multiple Expected Solution Points (ESPs) approach within the Characteristic Objects Method (COMET). This dual-dimensional approach tailors decision-making to align more closely with individual preferences and desired outcomes. Applied to the housing location selection problem, the proposed approach captures diverse user needs, delivering highly personalized and relevant recommendations. A comparative analysis with selected subjective weighting methods validates the stability and reliability of the multiple ESP-COMET model. By advancing personalization in decision support systems, this research offers a robust framework for more accurate and user-centered decision-making across complex scenarios.