This paper presents a solution to the challenges faced by contrastive learning in sequential recommendation systems. In particular, it addresses the issue of false negative, which limits the effectiveness of recommendation algorithms. By introducing an advanced approach to contrastive learning, the proposed method improves the quality of item embeddings and mitigates the problem of falsely categorizing similar instances as dissimilar. Experimental results demonstrate performance enhancements compared to existing systems. The flexibility and applicability of the proposed approach across various recommendation scenarios further highlight its value in enhancing sequential recommendation systems.

翻译：本文针对序列推荐系统中对比学习面临的挑战提出解决方案，重点解决限制推荐算法有效性的假阴性问题。通过引入先进的对比学习方法，所提方法提升了物品嵌入质量，缓解了将相似实例错误归类为异类的现象。实验结果表明，该方法相较现有系统取得了性能提升。所提方法在不同推荐场景中展现的灵活性与适用性，进一步凸显其在增强序列推荐系统方面的价值。