Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation. This model enhances the robustness of recommendations by mixing three different basic recommendation technologies. Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively. Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse.

推荐系统（RS）是一种向用户提供准确推荐的技术。但是，仅考虑建议的准确性并不全面，因为用户有不同的要求。为了提高综合性能，本文提出了一种基于多目标优化的混合推荐模型，可以同时优化推荐的准确性，多样性，新颖性和覆盖范围。该模型通过混合三种不同的基本推荐技术来增强推荐的鲁棒性。此外，我们使用多目标进化算法（MaOEA）对其进行了求解，并对其进行了广泛的测试。实验结果证明了该模型的有效性，该模型可以在准确和多样的基础上为推荐提供更多新颖的项目。