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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.

Data normalization is a fundamental part of preprocessing in decision-making problems. It enables aggregating and comparing different decision attributes using Multi-Criteria Decision Making (MCDM) methods. However, the choice of normalization technique significantly influences the final ranking. Different rankings are obtained using different normalization techniques for the same alternatives and methods. At the same time, the rank-reversal phenomenon is observed in many popular MCDM methods. This paper addresses the challenge of developing the MCDA method, which is rank-reversal free and solves the choosing normalization technique problem. We propose a new method called Compromise-COMET (C-COMET), which identifies the adaptive decision model based on many normalization techniques and finds a compromise solution. The Characteristic Objects METhod (COMET) has been modified to include a voting algorithm to assess characteristic objects. This approach is distinct from previous hybrid approaches, which integrated different MCDA methods with COMET since the main focus of this work is to obtain a compromise solution based on indirect voting. It means that instead of voting for the ranking of alternatives (like in the Copeland, Borda, or the Rank Position Method (RPM)), we vote for the preferences of characteristic objects. We identify a continuous assessment model free of the rank-reversal phenomenon in the result. Our investigation shows a study case where the Compromise-COMET method is considered with the TOPSIS method in detail. Afterward, we present more analysis for using C-COMET with Weighted Aggregated Sum Product ASSessment (WASPAS), COmbined COmpromise SOlution (COCOSO), MultiAttributive Ideal-Real Comparative Analysis (MAIRCA), and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) methods to show the robustness of our proposal approach. The C-COMET algorithm models results with high values of weighted Spearman correlation and the weighted similarity (WS) coefficient, confirming our proposal’s accuracy.