Author(s)

Fengqin He

Corresponding Author

Fengqin He

Abstract

When dealing with big data, the recommendation performance of traditional recommendation systems, such as conventional collaborative filtering, is limited. The use of easy-to-use K-means clustering algorithm combined with collaborative filtering constitutes a recommendation algorithm. This paper uses genetic algorithm to optimize the combined recommendation algorithm, simplify the combined recommendation algorithm, and reduce the complexity and cost of the combination algorithm. At the same time, the genetic algorithm is improved to enhance the optimization ability of the genetic algorithm and improve the performance of the recommendation system. Finally, the performance of the proposed algorithm is tested based on MovieLens movie scoring dataset. The results show that the optimization ability of the genetic algorithm is improved and the performance of the recommendation system is improved.

Keywords

Recommendation System, Massive Data, Research Prospect