Models for Calculating Weights for Estimation Innovative Technical Objects

Titenko, Evgeniy A.; Frolov, N. S.; Khanis, A. L.; Добросердов, О. Г.; Zakharenkov, A. I.; Popov, A.; Dronova, Tetiana

doi:10.15588/1607-3274-2020-3-17

Cited by 2 publications

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“…The analytic hierarchy process (AHP) and its generalization -the analytic network process (ANP) methods are applied in various fields such as a decision-making in industry, ecology, medicine, health protection, politics, social sphere, finances, in economic forecasting and other (Aidinidou, Kaparis, & Georgiou, 2023;Bakioglu & Atahan, 2021;Fernandez, Paringit, Salvador, Lucero, & Galupino, 2020;Ho & Ma, 2018;Ikram, Sroufe, & Zhang, 2020;Jamali, Feylizadeh, & Liu, 2021;Kavilal, Venkatesan, & Kumar, 2017;Khan, Shameem, Kumar, Hussain, & Yan, 2019;Liu, Eckert, & Earl, 2020;Mangla, Kumar, & Barua, 2015;Matin, Zare, Ghotbi-Ravandi, & Jahani, 2020;Melnyk, Hlushko, & Borysova, 2020;Mu, Cooper, & Peasley, 2020;Shi & Lai, 2023;Titenko et al, 2020). A typical hierarchical or network model for a practical decision-making problem contains decision criteria, sub-criteria, goals, subgoals and decision alternatives (Saaty, 1980).…”

Section: Literature Reviewmentioning

confidence: 99%

Multicriteria evaluation of recommender systems using fuzzy analytic hierarchy process

Nedashkovskaya,

Androsov

2023

Preprint

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Offering relevant items and goods has always been a core issue for consumer-centric business. In recent years the digitalization process gave such entities a significant push towards implementing computer-aided systems for solving recommendations generation problems via applying statistical learning methods on large scale datasets of user-item interactions. Since then, there were developed a variety of algorithms for a recommender system design, based on neighborhood metrics, matrix factorization machines and deep neural networks, each of them having their application criteria and requirements on input data. The purpose of the following research is to investigate performance of graph neural networks recommender systems on two tasks – recommendation retrieval and next best offer prediction. As a result, the graph-based recommender system for solving both tasks, described above, was synthesized and benchmarked on open-source e-commerce dataset. From the conducted experiment it was gained evidence that proposed approach is feasible to solve recommendation retrieval tasks and outperform models, that are considered de-facto standard, i.e., factorization machines and recurrent neural netwoks.

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Section: Literature Reviewmentioning

confidence: 99%

Multicriteria evaluation of recommender systems using fuzzy analytic hierarchy process

Nedashkovskaya,

Androsov

2023

Preprint

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“…Fuzzy sets as the elements of the pairwise comparisons matrix can be used in the decision making problem, whenever the DM is not sure about the preference degree of his/her evaluations of the pairs in question. We can primarily find them in Fuzzy Analytic Hierarchy Process (FAHP), PROMETHEE and TOPSIS oriented methods, and various exact and heuristic algorithms, [16,22,32]. They are also useful in aggregating pairwise comparisons, particularly in consensus group decision making problems, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Deriving priority vector from pairwise comparisons matrix with fuzzy elements by solving optimization problem

Ramík

2023

OPSEARCH

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Pairwise comparisons matrix with fuzzy elements (FPCM) are appropriate for the decision makers who are uncertain about the relative importance of elements. We can primarily find them in Fuzzy Analytic Hierarchy Process, PROMETHEE, TOPSIS methods, and many exact and heuristic algorithms. They are also useful in aggregating pairwise comparisons, particularly in consensus group decision making problems and they form the basis for many decision-making models as intuitionistic fuzzy relations, pythagorean, q-rung orthopair fuzzy preference relations, hesitant or interval fuzzy sets, and also stochastic judgments. Here, the decision model is formulated by investigating pairwise comparisons matrices (PCMs) with elements from abelian linearly ordered group (alo-group), which enables unifying multiplicative, additive and fuzzy PCMs. Then we define a novel concept of consistency, coherence and intensity of FPCMs, and propose a number of optimization methods for finding a consistent vector, coherent vector and intensity vector of a FPCM satisfying the desirable properties. Finally, two illustrating examples are discussed.

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Models for Calculating Weights for Estimation Innovative Technical Objects

Cited by 2 publications

References 0 publications

Multicriteria evaluation of recommender systems using fuzzy analytic hierarchy process

Multicriteria evaluation of recommender systems using fuzzy analytic hierarchy process

Deriving priority vector from pairwise comparisons matrix with fuzzy elements by solving optimization problem

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