Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems

Abstract

Since the rise of E-commerce companies and many other web services, the applications of recommender systems have been adopted more broadly than ever before. Although collaborative filtering is the most well-known approach which utilizes customer’s preference to discover their interest, the problems of data sparsity and similarities selection still exist in it. Thus, this study intends to propose a hybrid metaheuristic with perturbation-based K-nearest neighbors and densest imputation for collaborative filtering (KDI-KNN) algorithm to reduce the effects of data sparsity. A similarities union function is proposed to determine the fittest similarity and enhance the prediction performance. Eventually, the experimental results indicate that hybrid metaheuristics with perturbation-based KDI-KNN algorithms are superior to basic KNN, original KDI-KNN, and most single metaheuristic-based KDI-KNN. In addition, a real-world dataset, fund transaction dataset is adopted in the case study. The analysis reveals that the similarity is seriously affected by the different content of the dataset.