With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. However, traditional collaborative filtering and content-based recommendation methods have limitations in dealing with data sparsity and cold start problems, especially in the face of largescale heterogeneous data, which makes it difficult to meet user expectations. This paper proposes a new label recommendation algorithm based on metric learning, which aims to overcome the challenges of traditional recommendation systems by learning effective distance or similarity metrics to capture the subtle differences between user preferences and item features. Experimental results show that the algorithm outperforms baseline methods including local response metric learning (LRML), collaborative metric learning (CML), and adaptive tensor factorization (ATF) based on adversarial learning on multiple evaluation metrics. In particular, it performs particularly well in the accuracy of the first few recommended items, while maintaining high robustness and maintaining high recommendation accuracy.

翻译：随着数字信息的快速增长，个性化推荐系统已成为互联网服务不可或缺的组成部分，尤其在电子商务、社交媒体和在线娱乐领域。然而，传统协同过滤与基于内容的推荐方法在处理数据稀疏性和冷启动问题上存在局限，特别是在面对大规模异构数据时，难以满足用户期望。本文提出一种基于度量学习的新型标签推荐算法，旨在通过学习有效的距离或相似性度量来捕捉用户偏好与物品特征间的细微差异，从而克服传统推荐系统的挑战。实验结果表明，该算法在多项评估指标上优于包括局部响应度量学习（LRML）、协同度量学习（CML）以及基于对抗学习的自适应张量分解（ATF）在内的基线方法。尤其在推荐列表前几项的准确率方面表现突出，同时保持了较高的鲁棒性，并维持了高水平的推荐精度。