长尾群组推荐的免疫多目标优化实现

doi:10.3969/j.issn.1001-2400.2018.03.020

Abstract

Abstract:

Traditional group recommendations pay more attention to accuracy, which ignores the importance of long tail items. Considering the above problem, a long tail group recommendation algorithm is proposed. However, long tail recommendations reduce the accuracy of recommendation systems. In this paper, the group recommendation is modeled as a multiobjective problem. The group user satisfaction and item popularity are used as objective functions, and the immune operators such as coding, crossover and mutation are designed for multiobjective recommendation. Then, the long tail group recommendation is optimized by the immune multiobjective algorithm. Experimental results show that the proposed algorithm improves the diversity and novelty of the recommendation results while maintaining the accuracy of the group recommendation.

Key words: recommendation systems, multiobjective optimization, immune algorithm, long tail theory

HAN Yamin;CHAI Zhengyi;LI Yalun;ZHU Sifeng. Immune multiobjective optimization on long tail group recommendation[J].Journal of Xidian University, 2018, 45(3): 109-116.

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Immune multiobjective optimization on long tail group recommendation

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