Research on Hybrid Recommendation Algorithm based on Collaborative Filtering and Spearman Rank Correlation Coefficient

Abstract

A recommendation system is an information filtering tool that helps users find the products or services they need from a large amount of information. However, collaborative filtering is quite sensitive to data sparsity and cold start problems, and it may encounter certain difficulties when handling outliers. To address the issue of handling outliers, it is necessary to study and improve the existing collaborative filtering techniques. This paper proposes a personalized recommendation algorithm that integrates collaborative filtering with the Spearman rank correlation coefficient. By combining collaborative filtering and the Spearman rank correlation coefficient, the algorithm uses the latter to handle outliers, making it more suitable for nonlinear relationships. This hybrid recommendation algorithm can better handle outliers while maintaining personalized recommendations, providing a basis and reference for personalized recommendations.