近日，实验室张晓堃博士关于推荐系统的研究成果被人工智能领域顶级期刊Pattern Recognition (PR)录用，Pattern Recognition为CCF推荐B类期刊，中科院一区期刊，影响因子7.5。

题目：Rethinking Contrastive Learning in Session-based Recommendation

摘要：Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consistency of augmented views; (3) they treat all positive-negative signals equally, without considering their varying utility. To this end, we propose a novel multi-modal adaptive contrastive learning framework called MACL for session-based recommendation. In MACL, a multi-modal augmentation is devised to generate semantically consistent views at both item and session levels by leveraging item multi-modal features. Besides, we present an adaptive contrastive loss that distinguishes varying contributions of positive-negative signals to improve self-supervised learning. Extensive experiments on three real-world datasets demonstrate the superiority of MACL over state-of-the-art methods.

译文：会话推荐旨在根据匿名用户的有限行为预测其意图。对比学习因其在缓解数据稀疏性方面的能力，近年来在该任务中广受关注。然而，我们发现现有的基于对比学习的方法存在三个局限性：（1）它们忽视了物品级别的稀疏性，主要关注序列级别的稀疏性；（2）它们通常通过裁剪、遮蔽和重排等操作，仅基于物品ID对会话序列进行增强，未能保证增强视图之间的语义一致性；（3）它们对所有正负样本信号一视同仁，未考虑其贡献度的差异。为此，本文提出了一种多模态自适应对比学习框架，名为MACL。在MACL中，本文设计了一种多模态增强方法，利用物品的多模态特征，在物品级和会话级生成语义一致的视图。此外，本文提出了一种自适应对比损失函数，用于区分正负样本信号的不同贡献，以提升自监督学习效果。在三个真实世界数据集上的大量实验表明，MACL在性能上优于现有的基线模型。

论文地址: https://arxiv.org/abs/2506.05044