Most recommendation systems often encounter issues when dealing with sparse datasets, resulting in low prediction accuracy. Traditional methods struggle significantly when data is too sparse, affecting their ability to provide accurate recommendations. This paper proposes an approach using energy distance to analyze the structure within sparse datasets. The model’s effectiveness is demonstrated through experiments comparing it with traditional methods such as Cosine Similarity and Pearson Correlation across varying levels of sparsity in the MovieLens dataset.