JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (3): 75-85.doi: 10.6040/j.issn.1671-9352.2.2024.089

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Prototype-based recommendation method with uniformity constraints and importance balancing

CAO Yuxiang, LIAN Tao*, WANG Long, JING Xingbo, DOU Haocheng   

  1. College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
  • Published:2026-03-18

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Abstract: Prototype-based recommendation algorithms can achieve explainable recommendations by learning a set of user prototypes(or item prototypes)that represent typical preferences or common characteristics, as well as the association strength between users(or items)and prototypes. However, existing algorithms overlook the differences between prototypes and the load balancing among them, and hence cannot fully release the expressive power of the model. Therefore, a prototype-based recommendation method ProtoMF++ with uniformity constraints and importance balancing is developed on top of ProtoMF. This method added uniformity constraints between user prototypes(or item prototypes)and enhanced the differences between prototypes by minimizing the logarithm of the average pairwise Gaussian potential between prototype representations. In addition, the load importance of each prototype is defined as the total association strength between it and all users(or items), and the coefficient of variation of their load importance is minimized to realize importance balancing across different prototypes. Experiments are conducted on three benchmark datasets, and the results show that ProtoMF++ outperforms existing prototype-based recommendation methods. For example, on the Baby dataset, the values of HitRatio@10 and NDCG@10 increase by 4.74% and 10.64%, respectively.

Key words: recommender system, prototype representation, uniformity constraint, importance balancing

CLC Number: 

  • TP391
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