Journal of Multi-Criteria Decision Analysis最新文献

Transparency in computing is an important precondition to ensure the trust of users. One concrete way of delivering transparency is to provide explanations of computing results. To this end, we introduce a method for explaining the results of various linear and hierarchical multi-criteria decision-making (MCDM) techniques such as the weighted sum model (WSM) and the analytic hierarchy process (AHP). The two key ideas are (A) to maintain a fine-grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high-level comparison of two alternatives in an MCDM problem, presumably an optimal and a non-optimal one, illuminating why one alternative was preferred over the other. We show the usefulness of our techniques by generating explanations for two well-known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.

In the domain of context-aware recommender systems, understanding and leveraging feature interactions is crucial for enhancing recommendation quality. Feature interactions delve into the complex interdependencies among user characteristics, item attributes, and contextual factors like time and location. Traditional models often struggle to effectively combine these diverse features, potentially leading to suboptimal recommendations. To tackle this issue, we propose enhancing context-aware recommender systems through deep feature interaction learning. Our model, which combines BiLSTM and Hybrid Attention mechanisms, offers a sophisticated architecture designed to exploit deep feature interactions effectively. This approach ensures that our system captures essential contextual dynamics, thereby improving the effectiveness of the recommendation process. Experimental results across multiple datasets validate the efficacy of our approach, showing significant improvements in key metrics such as $��\mathrm{AUC}�$ and $��\text{LogLoss}�$ compared to traditional and contemporary models. These achievements underscore our model's ability to deliver nuanced and adaptively tailored recommendations, marking a valuable contribution to the field of recommender systems.

Multiobjective optimization problems involve several conflicting objective functions to be optimised simultaneously and solutions to these problems represent different trade-offs. When applying interactive methods, a decision maker with domain expertise provides one's preference information over several iterations, according to which new solutions are computed until finding a solution with the most preferred trade-offs. Publications on interactive multiobjective optimization methods mainly focus on the optimisation algorithm, and little attention is paid to their implementations, not to mention the development of user interfaces that enable interaction with the decision maker. User interfaces involve icons but there are no studies about icons for the specific functionalities of multiobjective optimization methods. Icons convey meaning effectively to users interacting with technology. With these small pictorial representations, information on system functionalities is communicated quickly. However, the immediacy of icon recognition can also lead to misunderstandings and difficulties in using the system if they are not designed properly. Semantic distance in icon design indicates the closeness of the pictorial representation to its intended functionality and, thus, functions as the main principle in designing effective icons. An empirical study ($��N�=�38�$) was conducted to examine the semantic distances of icons for interactive multiobjective optimization methods implemented in an open-source software framework. The study addressed the main functionalities. According to our main findings, we suggest an icon set for the considered functionalities, to enable fluent interaction with decision makers and other involved parties utilising interactive multiobjective optimization methods via user interfaces.

In this study, by using the concepts of (strictly) strongly convexity and preconvexity, associated with controlled multiple integral type functionals, and a mean value type theorem, we formulate some connections between new classes of generalised Minty (weak) variational inequalities of vector-type and the corresponding multiple-objective extremization problems. The considered classes of variational models are motivated by their applications in real-world modelling problems. The presence of control variables and controlled multiple integrals are the main tools in establishing the new outcomes.

In this paper, a new concept of generalised convexity and a new class of exact exponential penalty method, namely the concept of $��\left(�p�,�r�\right)�$-E-invexity and $��l_1�$ exact exponential penalty E-function method, respectively are introduced for (not necessarily) differentiable vector optimization problem in which functions are E-differentiable. The conditions governing the equivalence between sets of (weak) efficient solutions of the original constrained E-differentiable vector optimization problem and of its associated unconstrained exponential penalised vector optimization problem are studied. Examples are given to illustrate the obtained results.

Targeted medical decision-making is a current strategy for addressing the heterogeneity in the patient population, especially when patients' preferences are included in the decision-making process. In this paper, we propose a user-customizable hybrid framework that can be adjusted at the patient group level to target a medical decision process. Our framework provides a flexible design, capable of balancing the gain from the reduction of provider time against the cost of prediction inaccuracy resulting from group customization. The framework combines a descriptive process, used to group the patients based on preference-based subjective features, with a predictive process, which uses objective features to match a new patient with a group. We illustrate our approach by applying it to the colorectal cancer-screening problem. The provider chooses what level of trade-off is appropriate, as a function of the acceptable error level. The group customization process allows decision makers to better allocate scarce resources, by potentially shortening the time-consuming process of modelling patients' preferences using individualised stability analysis. The proposed framework might be applied, with minor changes, to various medical decisions, or even to broader provider-user scenarios, in which targeted decision-making that includes user preferences is advantageous.

In today's dynamic business landscape, organisations increasingly realise the pivotal role of innovative marketing approaches (IMA) in gaining a competitive edge and driving sustainable growth. However, many organisations encounter formidable barriers to adopting and developing IMA, despite the growing importance of innovative approaches in marketing. The purpose of this research is to identify, prioritise, establish the inter-relationships and propose a ‘level-wise structural model’ of the barriers that an organisation faces in adopting and developing IMA. The research employed a mix of quantitative and qualitative methods. An exhaustive review of related literature and theoretical frameworks (namely the ‘resource-based view’ (RBV) theory, the ‘social capital theory’ (SCT) theory, the ‘innovation resistance theory’ (IRT) theory, and the ‘institutional theory’ (IT) theory), along with expert feedback, are used to categorise and finalise the barriers. Further, a case study from ‘micro, small and medium enterprises’ (MSMEs) in a developing nation such as India along with hybrid ‘multi-criteria decision-making’ (MCDM), that is, best-worst method (BWM), was applied to prioritise the barriers. While ‘interpretive structural modelling’ (ISM) was used to establish contextual relationships, and ‘matrice d'impacts croisés multiplication appliquée á un classment’ (MICMAC) analysis was employed to cluster the barriers. The findings suggest that among the main category technology-related barriers and from subcategories, infrastructure supports the top barriers and works as a driving obstacle while building IMA for sustainable development. The findings from this study can help develop effective marketing strategies that align profitability with environmental and social responsibility.

This paper investigates a family of multicost variational models driven by H-type I functionals. Concretely, under H-type I assumptions of the involved functionals, we state and prove necessary and sufficient criteria of efficiency for a feasible point in the considered class of controlled variational models.

MSC 2020 Classification: 49K20, 58E17, 58E25, 90C30