Clustering-based recommendation method with enhanced grasshopper optimisation algorithm

In the era of big data, personalised recommendation systems are essential for enhancing user engagement and driving business growth. However, traditional recommendation algorithms, such as collaborative filtering, face significant challenges due to data sparsity, algorithm scalability, and the difficulty of adapting to dynamic user preferences. These limitations hinder the ability of systems to provide highly accurate and personalised recommendations. To address these challenges, this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm (GOA), termed LCGOA, to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment. By combining the K-means algorithm with the enhanced GOA, which incorporates a Lévy flight mechanism and multi-strategy co-evolution, our method overcomes the centroid sensitivity issue, a key limitation in traditional clustering techniques. Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy, offering more relevant content to users and driving greater customer satisfaction and business growth.