A Multi-objective Recommendation Algorithm Based on User Stratification

The classic recommendation system focuses on the accuracy of recommendation. With the increase of users’ diversified needs, the diversity of recommendation results has attracted more and more attention. The accuracy and diversity of the recommendation always conflict with each other, and the traditional recommendation algorithms often ignore the difference of users’ activity in the system. A user stratification multi-objective recommendation algorithm is proposed based on the difference of users’ evaluation times on the items, which can provide better recommendation results for different users. Users are divided into three types with high, medium and low evaluation times, and three different initialization methods are proposed for the algorithm. By the comparative analysis of the initialization operations and parameter settings of the existing probability based multi-objective evolutionary algorithm, improved crossover and mutation operations are obtained. The experimental results verify that the enhanced multi-objective evolutionary algorithm can find better results with higher accuracy and diversity. Finally, a recommendation scheme based on user stratification is summarized, which helps to improve the recommendation effect for different users.