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

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 KDIKNN. 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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

With the rise of E-commerce and web services, recommender systems are widely adopted, a new hybrid metaheuristic algorithm is proposed to reduce data sparsity effects, and experimental results show its superiority over other methods.