A hybrid recommendation system with many-objective evolutionary algorithm - Cai, Xingjuan; Hu, Zhaoming; Zhao, Peng; Zhang, WenSheng; Chen, Jinjun (Expert systems with applications, 2020)
•Recommend the more and novel items based on accurate and diverse recommendations.•Mixing multiple recommendation technologies to improve recommendation performance.•The system is based on rating, which makes the recommendation more objective.•Clustering strategies are used to reduce recommended consumption. 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.