分离对比学习与课程调度在跨域推荐匹配中的应用
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling
AI总结：
针对跨域推荐中混合对比学习训练不稳定的问题，提出SCCDR框架，通过分离域内与域间对比学习及课程调度策略，提升表示质量，在多个数据集和在线测试中达到最优性能。
摘要：
跨域推荐（CDR）是一项旨在通过利用源域信息来提升目标域推荐性能的任务。在CDR的匹配阶段，对比学习方法已被广泛用于域内（intra-CL）和域间（inter-CL）用户/物品的表示学习与知识迁移。然而，我们观察到，直接在混合的域内和域间对比学习任务上应用对比学习，忽略了从域间学习相对于从域内学习的难度，从而可能导致严重的训练不稳定性。这种不稳定性会恶化表示学习过程，并损害所生成嵌入的质量。为此，我们提出了一个名为SCCDR的新框架，该框架基于分离的域内和域间对比学习范式以及停止梯度操作来处理这一缺陷。具体而言，SCCDR包含两个专门的课程阶段：域内-域间分离和域间课程调度。前一阶段在源域和目标域中分别使用两个不同的对比视图进行域内对比学习任务。同时，后一阶段通过考虑由重叠用户锚定的负样本难度来推导有效课程，从而有策略地处理域间对比学习任务。在多个开源数据集、一个从真实推荐系统中提取的离线专有工业数据集以及在线A/B测试上的实证实验验证了SCCDR在多个基线上达到了最先进的性能。
英文摘要：
Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains. Contrastive learning methods have been widely adopted among intra-domain (intra-CL) and inter-domain (inter-CL) users/items for their representation learning and knowledge transfer during the matching stage of CDR. However, we observe that directly employing contrastive learning on mixed-up intra-CL and inter-CL tasks ignores the difficulty of learning from inter-domain over learning from intra-domain, and thus could cause severe training instability. Therefore, this instability deteriorates the representation learning process and hurts the quality of generated embeddings. To this end, we propose a novel framework named SCCDR built up on a separated intra-CL and inter-CL paradigm and a stop-gradient operation to handle the drawback. Specifically, SCCDR comprises two specialized curriculum stages: intra-inter separation and inter-domain curriculum scheduling. The former stage explicitly uses two distinct contrastive views for the intra-CL task in the source and target domains, respectively. Meanwhile, the latter stage deliberately tackles the inter-CL tasks with a curriculum scheduling strategy that derives effective curricula by accounting for the difficulty of negative samples anchored by overlapping users. Empirical experiments on various open-source datasets and an offline proprietary industrial dataset extracted from a real-world recommender system, and an online A/B test verify that SCCDR achieves state-of-the-art performance over multiple baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
