Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations. However, these contrastive objectives: (1) serve a similar role as the cross-entropy loss while ignoring the item representation space optimisation; and (2) commonly require complicated modelling, including complex positive/negative sample constructions and extra data augmentation. In this work, we introduce Self-Contrastive Learning (SCL), which simplifies the application of CL and enhances the performance of state-of-the-art CL-based recommendation techniques. Specifically, SCL is formulated as an objective function that directly promotes a uniform distribution among item representations and efficiently replaces all the existing contrastive objective components of state-of-the-art models. Unlike previous works, SCL eliminates the need for any positive/negative sample construction or data augmentation, leading to enhanced interpretability of the item representation space and facilitating its extensibility to existing recommender systems. Through experiments on three benchmark datasets, we demonstrate that SCL consistently improves the performance of state-of-the-art models with statistical significance. Notably, our experiments show that SCL improves the performance of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and 11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks. Additionally, our analysis elucidates the improvement in terms of alignment and uniformity of representations, as well as the effectiveness of SCL with a low computational cost.

翻译：会话式推荐旨在根据已有的物品交互序列预测用户下一个感兴趣物品，对比学习在此领域的应用日益广泛，通过改进用户和物品表示提升了推荐性能。然而，现有对比学习目标函数存在以下问题：（1）与交叉熵损失功能相似，却忽视了物品表示空间优化；（2）通常需要复杂的建模流程，包括正负样本构建和额外数据增强。本文提出自对比学习（Self-Contrastive Learning, SCL），该方法简化了对比学习的应用流程，同时提升了当前最先进对比学习推荐技术的性能。具体而言，SCL被设计为直接促进物品表示均匀分布的目标函数，可高效替代现有最优模型中的所有对比学习组件。与以往方法不同，SCL无需任何正负样本构建或数据增强操作，增强了物品表示空间的可解释性，并便于扩展到现有推荐系统。在三个基准数据集上的实验表明，SCL能以统计显著性一致提升现有最优模型的性能。值得注意的是，实验结果显示，SCL使两个最优模型在P@10（精确率）上平均提升8.2%和9.5%，在MRR@10（平均倒数排名）上平均提升9.9%和11.2%。此外，我们的分析阐明了SCL在表示对齐度与均匀性方面的改善效果，以及其低计算成本下的有效性。