Evolutionary and Swarm Intelligence Approaches for Recommender Systems

Dear Colleagues,

The proposed Special Issue focuses on the integration of recommender systems and metaheuristic algorithms, aiming to advance global optimization, multi-objective decision making, and intelligent personalization in data-driven recommendation environments. Traditional recommendation techniques—such as collaborative filtering, matrix factorization, and deep or graph-based recommenders—often face challenges including local optima, parameter sensitivity, and trade-offs between accuracy, diversity, novelty, and fairness. Metaheuristic optimization methods (e.g., genetic algorithms, particle swarm optimization, ant colony optimization, grey wolf optimization, and hybrid evolutionary strategies) provide a flexible and powerful framework for addressing these issues through adaptive search and multi-objective exploration.

The scope of this collection covers theoretical and applied research that leverages metaheuristic, evolutionary, and swarm intelligence algorithms to enhance or automate key stages of recommender systems. Topics include, but are not limited to, the following: (1) metaheuristic-based hyperparameter and structure optimization for deep, graph, and sequential recommenders; (2) multi-objective optimization of accuracy–diversity–fairness trade-offs; (3) surrogate-assisted or hybrid learning–metaheuristic approaches for large-scale and online recommendation; (4) domain-specific and constrained recommendation (e.g., route, course, or service recommendation); and (5) applications in industrial, educational, e-commerce, and social computing contexts.

The purpose of the Special Issue is to create an interdisciplinary platform for bridging optimization theory, artificial intelligence, and recommendation technologies. While prior literature has independently matured in both recommender systems and metaheuristic optimization, comprehensive studies uniting the two fields remain limited. This topical collection will therefore fill a gap by highlighting recent methodological advances, benchmarking practices, and practical implementations that demonstrate how metaheuristic algorithms can improve the adaptability, scalability, and fairness of modern recommender systems.

Dr. Haichuan Yang
Prof. Dr. Shangce Gao
Prof. Dr. Ancai Zhang
Dr. Qin Chang
Guest Editors

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• recommender systems
• metaheuristic optimization
• evolutionary algorithms
• multi-objective optimization
• intelligent personalization
• swarm intelligence
• fairness and diversity in recommendation