Personalized Recommendation Based on Evolutionary Multi-Objective Optimization


Traditional recommendation techniques in recommender systems mainly focus on improving recommendation accuracy. However, personalized recommendation, which considers the multiple needs of users and can make both accurate and diverse recommendations, is more suitable for modern recommender systems. In this paper, the task of personalized recommendation is modeled as a multi-objective optimization problem. A multiobjective recommendation model is proposed. The proposed model maximizes two conflicting performance metrics termed as accuracy and diversity. The accuracy is evaluated by the probabilistic spreading method, while the diversity is measured by recommendation coverage. The proposed MOEA-based recommendation method can simultaneously provide multiple recommendations for multiple users in only one run. Our experimental results demonstrate the effectiveness of the proposed algorithm. Comparison experiments also indicate that the proposed algorithm can make more diverse yet accurate recommendations.