Most existing contrastive learning-based sequential recommendation (SR) methods rely on random operations (e.g., crop, reorder, and substitute) to generate augmented sequences. These methods often struggle to create positive sample pairs that closely resemble the representations of the raw sequences, potentially disrupting item correlations by deleting key items or introducing noisy iterac, which misguides the contrastive learning process. To address this limitation, we propose Learnable sequence Augmentor for triplet Contrastive Learning in sequential Recommendation (LACLRec). Specifically, the self-supervised learning-based augmenter can automatically delete noisy items from sequences and insert new items that better capture item transition patterns, generating a higher-quality augmented sequence. Subsequently, we randomly generate another augmented sequence and design a ranking-based triplet contrastive loss to differentiate the similarities between the raw sequence, the augmented sequence from augmenter, and the randomly augmented sequence, providing more fine-grained contrastive signals. Extensive experiments on three real-world datasets demonstrate that both the sequence augmenter and the triplet contrast contribute to improving recommendation accuracy. LACLRec significantly outperforms the baseline model CL4SRec, and demonstrates superior performance compared to several state-of-the-art sequential recommendation algorithms.

翻译：现有大多数基于对比学习的序列推荐方法依赖随机操作（如裁剪、重排序和替换）来生成增强序列。这些方法往往难以创建与原始序列表示高度相似的正样本对，可能因删除关键项目或引入噪声交互而破坏项目相关性，从而误导对比学习过程。为克服这一局限，本文提出用于序列推荐中三元组对比学习的可学习序列增强器。具体而言，基于自监督学习的增强器能够自动从序列中删除噪声项目，并插入能更好捕捉项目转移模式的新项目，从而生成更高质量的增强序列。随后，我们随机生成另一增强序列，并设计基于排序的三元组对比损失来区分原始序列、增强器生成的增强序列与随机增强序列之间的相似度，提供更细粒度的对比信号。在三个真实数据集上的大量实验表明，序列增强器与三元组对比机制均有助于提升推荐准确性。本方法显著优于基线模型CL4SRec，并在与多种先进序列推荐算法的比较中展现出优越性能。