A novel multi-objective evolutionary algorithm for recommendation systems

Laizhong Cui, Peng Ou, Xianghua Fu, Zhenkun Wen, Nan Lu

Abstract: Highlights•A new topic diversity indicator is introduced, which can be used to measure various kinds of items in a recommendation list.•A new probabilistic multi-objective evolutionary algorithm (PMOEA) is presented, which is suitable for the recommendation systems.•A new crossover operator is proposed to generate new solution, called the multi-parent probability genetic operator.•The experimental results show that PMOEA can achieve a good balance between precision and diversity.