Advanced Decision Support and Recommender Systems

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

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