Application of Multi-objective Evolutionary Algorithm in E-Commerce Recommendation System

Traditional recommendation methods often have incomplete understandings of recommendation evaluation indicators. They are limited to the accuracy of recommendation, but often ignore the diversity, novelty, coverage and other indicators. Therefore, the recommendation system should use more evaluation indicators to meet personalized demands of users so as to obtain satisfactory results. This is closely related to the problem of multi-objective optimization. This paper describes the mathematical expression of the multi-objective optimization problem and uses NSGA-II algorithm to test its accuracy and diversity indexes of Netflix dataset. The experimental results show that NSGA-II algorithm can meet multi-objective requirements of the recommendation system and the experimental results are satisfactory.