# Contrastive Cross-domain Recommendation in Matching

arXiv: https://arxiv.org/abs/2112.00999 · HTML: https://mlsanity.com/paper/2112.00999/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2021.

## Built on by (most cited first, 25 of 25)

- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders](https://mlsanity.com/paper/2210.12316/) (2022)
- [Multi-view Multi-behavior Contrastive Learning in Recommendation](https://mlsanity.com/paper/2203.10576/) (2022)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [EasyRec: Simple yet Effective Language Models for Recommendation](https://mlsanity.com/paper/2408.08821/) (2024)
- [Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation](https://mlsanity.com/paper/2208.11457/) (2022)
- [A Collaborative Transfer Learning Framework for Cross-domain Recommendation](https://mlsanity.com/paper/2306.16425/) (2023)
- [Universal Item Tokenization for Transferable Generative Recommendation](https://mlsanity.com/paper/2504.04405/) (2025)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking](https://mlsanity.com/paper/2302.02636/) (2023)
- [Self-supervised Topic Taxonomy Discovery in the Box Embedding Space](https://mlsanity.com/paper/2408.15050/) (2024)
- [Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation](https://mlsanity.com/paper/2501.11671/) (2025)
- [Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.14438/) (2023)
- [Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains](https://mlsanity.com/paper/2306.01864/) (2023)
- [A Survey of Cross-domain Graph Learning: Progress and Future Directions](https://mlsanity.com/paper/2503.11086/) (2025)
- [Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System](https://mlsanity.com/paper/2406.17289/) (2024)
- [Review-Based Hyperbolic Cross-Domain Recommendation](https://mlsanity.com/paper/2403.20298/) (2024)
- [Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce](https://mlsanity.com/paper/2408.00346/) (2024)
- [Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling](https://mlsanity.com/paper/2502.16239/) (2025)
- [Generative Multi-Target Cross-Domain Recommendation](https://mlsanity.com/paper/2507.12871/) (2025)
- [VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation](https://mlsanity.com/paper/2507.21563/) (2025)
- [SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization](https://mlsanity.com/paper/2605.08809/) (2026)
- [DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction](https://mlsanity.com/paper/2606.10243/) (2026)
- [FedCGR: Federated Cross-Domain Generative Recommendation](https://mlsanity.com/paper/2608.10929/) (2026)

## Abstract (arXiv)

Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., candidate generation) module struggles with the data sparsity and popularity bias issues in both representation learning and knowledge transfer. In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR) framework for CDR in matching. Specifically, we build a huge diversified preference network to capture multiple information reflecting user diverse interests, and design an intra-domain contrastive learning (intra-CL) and three inter-domain contrastive learning (inter-CL) tasks for better representation learning and knowledge transfer. The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy, and neighbor aspects. In experiments, CCDR achieves significant improvements on both offline and online evaluations in a real-world system. Currently, we have deployed our CCDR on WeChat Top Stories, affecting plenty of users. The source code is in https://github.com/lqfarmer/CCDR.