Elite Evolutionary Discrete Particle Swarm Optimization for Recommendation Systems

Shanxian Lin, Yifei Yang, Yuichi Nagata () and Haichuan Yang ()
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Shanxian Lin: Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan
Yifei Yang: Faculty of Science and Technology, Hirosaki University, Hirosaki-shi 036-8560, Japan
Yuichi Nagata: Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan
Haichuan Yang: Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan

Mathematics, 2025, vol. 13, issue 9, 1-36

Abstract: Recommendation systems (RSs) play a vital role in e-commerce and content platforms, yet balancing efficiency and recommendation quality remains challenging. Traditional deep models are computationally expensive, while heuristic methods like particle swarm optimization struggle with discrete optimization. To address these limitations, this paper proposes elite-evolution-based discrete particle swarm optimization (EEDPSO), a novel framework specifically designed to optimize high-dimensional combinatorial recommendation tasks. EEDPSO restructures the velocity and position update mechanisms to operate effectively in discrete spaces, integrating neighborhood search, elite evolution strategies, and roulette-wheel selection to balance exploration and exploitation. Experiments on the MovieLens and Amazon datasets show that EEDPSO outperforms five metaheuristic algorithms (GA, DE, SA, SCA, and PSO) in both recommendation quality and computational efficiency. For datasets below the million-level scale, EEDPSO also demonstrates superior performance compared to deep learning models like FairGo. The results establish EEDPSO as a robust optimization strategy for recommendation systems that effectively handles the cold-start problem.

Keywords: recommendation system; metaheuristic algorithm; particle swarm optimization algorithm; elite evolution strategy; neighborhood search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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