Recommendation Based on Large-Scale Many-Objective Optimization for the Intelligent Internet of Things System

Abstract

Recommender systems are of great significance for mining the data generated by the Internet of Things (IoT) and are important for the intelligent IoT systems. The traditional recommendation algorithms only consider the accuracy as the optimization objective. In this article, a many-objective optimization model consisting of the F1 measure, recommendation novelty, recommendation coverage, customer satisfaction, landmark similarity, and overfitting is constructed for recommendation. Then, to improve the recommendation performance, we propose to use a large-scale many-objective optimization algorithm based on problem transformation (LSMaOA) to optimize the matrix factorization model for the recommender system in the intelligent IoT systems. The experimental results show that LSMaOA is robust and can effectively optimize the model’s six objectives. Compared with the knee point-driven evolutionary algorithm (KnEA), the grid-based evolutionary algorithm (GrEA), the large-scale multiobjective optimization framework (LSMOF), and the reference vector guided evolutionary algorithm (RVEA), the proposed algorithm can promote the F1 measure by 7.78%, 13.63%, 21.85%, and 28.63%, respectively.