提出MACL框架，解决会话推荐中对比学习的语义不一致和信号平权问题。
Rethinking Contrastive Learning in Session-based Recommendation
- 利用多模态特征生成语义一致的增强视图，兼顾物品与会话层级。
- 设计自适应对比损失，区分正负样本贡献度，提升学习效率。
- 在三个真实数据集上超越现有方法，适合推荐系统研究者参考。
会话推荐旨在基于有限行为预测匿名用户意图。尽管对比学习能缓解数据稀疏性，现有方法仍面临三大挑战：(1) 忽视物品级稀疏性，仅关注会话级稀疏性；(2) 通常使用物品ID进行裁剪、掩码或重排等增强，难以保证增强视图的语义一致性；(3) 对所有正负样本信号一视同仁，未考虑其不同效用。为此，我们提出一种新的多模态自适应对比学习框架MACL。MACL通过融合物品多模态特征，在物品和会话层面生成语义一致的增强视图；同时引入自适应对比损失，区分正负样本信号的差异贡献，以优化自监督学习。在三个真实世界数据集上的大量实验表明，MACL优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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