# Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling

arXiv: https://arxiv.org/abs/2502.16239 · HTML: https://mlsanity.com/paper/2502.16239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 67 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 30 of 38)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [Implicit Graph Neural Networks](https://mlsanity.com/paper/2009.06211/) (2020)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [Learning from Counterfactual Links for Link Prediction](https://mlsanity.com/paper/2106.02172/) (2021)
- [A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models](https://mlsanity.com/paper/1908.01297/) (2019)
- [Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View](https://mlsanity.com/paper/2102.10543/) (2021)
- [Adaptive Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2305.10837/) (2023)
- [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://mlsanity.com/paper/1909.00385/) (2019)
- [MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2008.02974/) (2020)
- [Contrastive Cross-domain Recommendation in Matching](https://mlsanity.com/paper/2112.00999/) (2021)
- [Cross-domain recommendation via user interest alignment](https://mlsanity.com/paper/2301.11467/) (2023)
- [Spectral Graph Attention Network with Fast Eigen-approximation](https://mlsanity.com/paper/2003.07450/) (2020)
- [Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction](https://mlsanity.com/paper/2106.02768/) (2021)
- [Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation](https://mlsanity.com/paper/2109.00368/) (2021)
- [Semi-Supervised Hierarchical Graph Classification](https://mlsanity.com/paper/2206.05416/) (2022)
- [Pre-training Graph Neural Network for Cross Domain Recommendation](https://mlsanity.com/paper/2111.08268/) (2021)

## Abstract (arXiv)

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.