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Applied Sciences
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Advanced Decision Support and Recommender Systems
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Advanced Decision Support and Recommender Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 3805

Special Issue Editor

Prof. Dr. Cuauhtémoc Sánchez Ramírez

E-Mail Website
Guest Editor
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba 94320, Veracruz, Mexico
Interests: supply chain management; supply chain simulation; system logistics and system dynamics modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A Decision Support System (DSS) is an information system that supports stakeholders in selecting responses to different alternatives. A DSS can aid human cognitive deficiencies by integrating various sources of information, providing intelligent access to relevant knowledge, and aiding the process of structuring decisions. Recommendation Systems (RSs) help users filter a large amount of information and generate a list of personalized suggestions to make more accurate decisions about their preferences. Both systems help in decision making and have been applied in different sectors, such as business, engineering, logistics, e-commerce, health, finances, government, and energy.

This Special Issue on “Advanced Decision Support and Recommender Systems” welcomes submissions of recent research work on this promising application area. The call is open to a broad thematic range of papers covering the recent applications and trends in Artificial Intelligence Techniques on DSS, Modeling and Simulation on DSS, Decision Support Systems for Industry 4.0 and 5.0, efficient trajectory and route recommender systems, innovative user interfaces for LLM-based Recommender Systems, evaluation of LLM-based Recommender Systems, and others.

Recommended topics include, but are not limited to, the following:

  • Social network analysis for decision making;
  • Design of soft computing techniques on DSS;
  • Implementation of big data analytics on DSS;
  • Advances in machine learning-based techniques for DSS;
  • Applications of intelligent decision support systems in the industry;
  • Impact of DSS on industrial performance;
  • Economic impact of DSS on the industry;
  • Strategic decision support systems in the supply chain;
  • Operation research applied to the industry;
  • Distributed and parallel data processing for location-based recommender systems;
  • Big spatiotemporal data management and analytic platforms for recommender systems;
  • Measurements and characterization of innovative context-aware recommender-system applications;
  • Data-driven solutions for location-based recommender system;
  • Multi-modal recommendation with LLMs;
  • Scalability and efficiency of LLM-based recommender systems;
  • Real-world deployments of LLMs in recommender systems.

Prof. Dr. Cuauhtémoc Sánchez Ramírez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • social network analysis
  • big data analytics
  • decision support system
  • recommender systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

Jump to: Review

49 pages, 8921 KiB  
Article
Theory of Faults (ToF): Numerical Quality Management in Complex Systems
by Niv Yonat and Igal M. Shohet
Appl. Sci. 2025, 15(2), 595; https://doi.org/10.3390/app15020595 - 9 Jan 2025
Viewed by 523
Abstract
The purpose of this manuscript is to provide general system theory concepts and practical tools for management under complexity. Built environments and infrastructure are produced, operated, and maintained by information systems; they are also integral components of information systems themselves. These systems are [...] Read more.
The purpose of this manuscript is to provide general system theory concepts and practical tools for management under complexity. Built environments and infrastructure are produced, operated, and maintained by information systems; they are also integral components of information systems themselves. These systems are self-organized and teleonomic. The complexity inherent in built environments and infrastructure systems poses a challenge to research, hindering forecasting and the implementation of managerial tools. The use of faults, which are complex systems’ responses to penetrating risk, provide us with databases of and windows into complex systems. This manuscript presents an explicatory theory (ToF), develops it mathematically, expands it through numerical experiments, validates it by case studies, and relates it to practice by expert contributions. A statistical analysis provides a phase parameter, descriptive statistics elucidate trending and emergent behaviors, digital signal processing expounds the effects of signals on information overload, and a directed-network analysis portray morphology, entropy, and time effects. The novelty of ToF is in the application of complexity theory to construction to produce data analysis tools and a managerial framework. Full article
(This article belongs to the Special Issue Advanced Decision Support and Recommender Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Fault progression through a system.</p> Full article ">Figure 2
<p>PDF(X) for <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> </mrow> </semantics></math> 1, different <math display="inline"><semantics> <mrow> <mi>μ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Power function generated using Equations (10) and (12) and Fun (1) for m = 50, <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi>X</mi> </mrow> <mrow> <mi>N</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> = 0.52, y = a∙x<sup>−b</sup>, a = 0.86, b = 1.84.</p> Full article ">Figure 3 Cont.
<p>Power function generated using Equations (10) and (12) and Fun (1) for m = 50, <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi>X</mi> </mrow> <mrow> <mi>N</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> = 0.52, y = a∙x<sup>−b</sup>, a = 0.86, b = 1.84.</p> Full article ">Figure 4
<p>Power function phase parameter as a function of <span class="html-italic">q</span>.</p> Full article ">Figure 5
<p>Log–log graph of power function.</p> Full article ">Figure 6
<p>Log–log graph of scaled power values, <span class="html-italic">b</span> = 1.946.</p> Full article ">Figure 7
<p>Information convolution in info-system. Entering signal (<b>a</b>), system response (<b>b</b>). Legend: Magnitude in % blue LN (5, 1), green LN (4.8, 1.6). Time—days.</p> Full article ">Figure 8
<p>Time effects via repetitions [“bifurcation”]. q = 0.0002 (<b>a</b>,<b>b</b>,<b>d</b>), q = 0.2 (<b>c</b>). Legend: X—number of states.</p> Full article ">Figure 9
<p>Cellular automaton, at time 15: graphical presentation (<b>a</b>), tabular presentation (<b>b</b>). Legend: Green, zero faults; yellow, up to ~10<sup>4</sup> faults; light red, ~10<sup>5</sup>; red ~10<sup>6</sup>.</p> Full article ">Figure 10
<p>A 1FB loop amplification of extreme events: the entering power function (<b>a</b>) and the amplification (<b>b</b>); 1FB loop amplification of an extreme event (<b>c</b>).</p> Full article ">Figure 11
<p>Information net morphology at t = 0 (<b>a</b>). Nodes degree distribution (<b>b</b>), their degree distribution power function, R<sup>2</sup> = 0.9365. Legend: Nodes represent autonomous agents (such as designers and subcontractors). GC = general contractor, SE = structure engineer.</p> Full article ">Figure 12
<p>Information morphology evolution after a redo order originating in a design error (<b>a</b>). Degree histogram (<b>b</b>) and degree probability distribution (R<sup>2</sup> = 0.94) (<b>c</b>) and hub centrality (<b>d</b>).</p> Full article ">Figure 13
<p>PDF for 27 projects. Legend: The histogram is the database distribution, in red the PDF.</p> Full article ">Figure 14
<p>Cumulative variance of 27 projects (<b>a</b>), accumulated by ordinal sequence of projects (<b>b</b>).</p> Full article ">Figure 15
<p>(<b>a</b>) Open rejects for three subprojects: (<b>b</b>) the IC marked by solid arrows and second IC marked by dashed arrows. Legend: Project “1”—civil engineering works; project “22”—systems; and project “23”—control.</p> Full article ">Figure 16
<p>Clockwise: (<b>a</b>) superposed number of faults histograms, (<b>b</b>) power function magnitude distribution for the whole database, confidence interval 95%, (<b>c</b>) segmented time to correction of faults and their S.D. analogy. Legend: <a href="#applsci-15-00595-f003" class="html-fig">Figure 3</a> jitter is proportional to open faults.</p> Full article ">Figure 17
<p>Power functions (<b>a</b>) for project 1 and (<b>c</b>) for project 19; a constant additive was omitted here. The histogram (<b>b</b>) pinpoints the time of avalanche in project 1 and the free Fourier transform of the project 1 error signal (<b>d</b>).</p> Full article ">Figure 18
<p>Network with fault category at the nodes (<b>a</b>), successive snapshots at different stages (<b>b</b>,<b>c</b>). Legend: Fault categories at the nodes; the brown arrows portray trajectories; the numbers portray precedence.</p> Full article ">Figure 19
<p>Scatter plot of open fault duration dates (<b>a</b>). Entropy trending (<b>b</b>). Legend: Timeline in months, entropy in Nats.</p> Full article ">
22 pages, 2317 KiB  
Article
Enhancing User Acceptance of an AI Agent’s Recommendation in Information-Sharing Environments
by Rebecca Kehat, Ron S. Hirschprung and Shani Alkoby
Appl. Sci. 2024, 14(17), 7874; https://doi.org/10.3390/app14177874 - 4 Sep 2024
Viewed by 1322
Abstract
Information sharing (IS) occurs in almost every action daily. IS holds benefits for its users, but it is also a source of privacy violations and costs. Human users struggle to balance this trade-off. This reality calls for Artificial Intelligence (AI)-based agent assistance that [...] Read more.
Information sharing (IS) occurs in almost every action daily. IS holds benefits for its users, but it is also a source of privacy violations and costs. Human users struggle to balance this trade-off. This reality calls for Artificial Intelligence (AI)-based agent assistance that surpasses humans’ bottom-line utility, as shown in previous research. However, convincing an individual to follow an AI agent’s recommendation is not trivial; therefore, this research’s goal is establishing trust in machines. Based on the Design of Experiments (DOE) approach, we developed a methodology that optimizes the user interface (UI) with a target function of maximizing the acceptance of the AI agent’s recommendation. To empirically demonstrate our methodology, we conducted an experiment with eight UI factors and n = 64 human participants, acting in a Facebook simulator environment, and accompanied by an AI agent assistant. We show how the methodology can be applied to enhance AI agent user acceptance on IS platforms by selecting the proper UI. Additionally, due to its versatility, this approach has the potential to optimize user acceptance in multiple domains as well. Full article
(This article belongs to the Special Issue Advanced Decision Support and Recommender Systems)
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Figure 1

Figure 1
<p>The framework of the UI optimization process.</p> Full article ">Figure 2
<p>Examples of various UI elements.</p> Full article ">Figure 2 Cont.
<p>Examples of various UI elements.</p> Full article ">Figure 3
<p>The distribution of the acceptance rate in the empirical study.</p> Full article ">Figure 4
<p>The acceptance rate for each factor separately.</p> Full article ">

Review

Jump to: Research

30 pages, 1029 KiB  
Review
A Meta-Analysis of the Review Literature on Multiple-Criteria Decision Aids for Environmental Issues
by Panagiota Digkoglou, Alexis Tsoukiàs, Jason Papathanasiou and Katerina Gotzamani
Appl. Sci. 2024, 14(23), 10862; https://doi.org/10.3390/app142310862 - 23 Nov 2024
Viewed by 1009
Abstract
Environmental decision making is a complex process that requires the consideration of multiple factors. Therefore, Multiple-Criteria Decision Aiding (MCDA) aims to address the challenges of environmental decision making. This paper analyses published review papers that discuss the use of MCDA in environmental problems, [...] Read more.
Environmental decision making is a complex process that requires the consideration of multiple factors. Therefore, Multiple-Criteria Decision Aiding (MCDA) aims to address the challenges of environmental decision making. This paper analyses published review papers that discuss the use of MCDA in environmental problems, with the goal of drawing useful meta-level conclusions. The review papers were categorised by application field and sorted by various criteria. The main findings of each paper were also analysed. The analysis reveals that MCDA publications in the specific domain have shown a strong upward trend. Hybrid MCDA is increasingly being applied as it can cope with the multidimensional challenges of environmental decision making. AHP appears to be the most widespread method. The sustainable energy sector is particularly interested in the use of MCDA. However, while decision-makers extensively use MCDA in environmental problems, its corresponding application in real-world settings is not always satisfactory. Full article
(This article belongs to the Special Issue Advanced Decision Support and Recommender Systems)
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Figure 1

Figure 1
<p>Research methodology.</p> Full article ">Figure 2
<p>Overview of review papers by year of publication.</p> Full article ">
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