# Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

arXiv: https://arxiv.org/abs/2105.04785 · HTML: https://mlsanity.com/paper/2105.04785/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [A Deep Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2009.06215/) (2020)
- [Graph Factorization Machines for Cross-Domain Recommendation](https://mlsanity.com/paper/2007.05911/) (2020)

## Built on by (most cited first, 24 of 24)

- [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Multi-view Multi-behavior Contrastive Learning in Recommendation](https://mlsanity.com/paper/2203.10576/) (2022)
- [Cross-domain recommendation via user interest alignment](https://mlsanity.com/paper/2301.11467/) (2023)
- [Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation](https://mlsanity.com/paper/2501.11671/) (2025)
- [ELECRec: Training Sequential Recommenders as Discriminators](https://mlsanity.com/paper/2204.02011/) (2022)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [Diffusion Cross-domain Recommendation](https://mlsanity.com/paper/2402.02182/) (2024)
- [DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation](https://mlsanity.com/paper/2412.15005/) (2024)
- [DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems](https://mlsanity.com/paper/2305.12058/) (2023)
- [Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity](https://mlsanity.com/paper/2403.06139/) (2024)
- [Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach](https://mlsanity.com/paper/2406.16494/) (2024)
- [Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing](https://mlsanity.com/paper/2407.12374/) (2024)
- [Modeling Churn in Recommender Systems with Aggregated Preferences](https://mlsanity.com/paper/2502.18483/) (2025)
- [Customized Conversational Recommender Systems](https://mlsanity.com/paper/2207.00814/) (2022)
- [MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2310.11088/) (2023)
- [Domain-Aware Cross-Attention for Cross-domain Recommendation](https://mlsanity.com/paper/2401.11705/) (2024)
- [Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement](https://mlsanity.com/paper/2404.04481/) (2024)
- [Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization](https://mlsanity.com/paper/2409.15568/) (2024)
- [SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling](https://mlsanity.com/paper/2503.04619/) (2025)
- [Churn-Aware Recommendation Planning under Aggregated Preference Feedback](https://mlsanity.com/paper/2507.04513/) (2025)
- [Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings](https://mlsanity.com/paper/2508.16210/) (2025)
- [CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2608.09580/) (2026)

## Abstract (arXiv)

Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary (source) domain to improve the performance of recommender system in the target domain. In these CDR approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very effective, which explicitly learn a mapping function from source embeddings to target embeddings with overlapping users. However, these approaches suffer from one serious problem: the mapping function is only learned on limited overlapping users, and the function would be biased to the limited overlapping users, which leads to unsatisfying generalization ability and degrades the performance on cold-start users in the target domain. With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage. In the transfer (pre-training) stage, a source model and a target model are trained on source and target domains, respectively. In the meta stage, a task-oriented meta network is learned to implicitly transform the user embedding in the source domain to the target feature space. In addition, the TMCDR is a general framework that can be applied upon various base models, e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct extensive experiments on 6 cross-domain tasks to demonstrate the superior performance and compatibility of TMCDR.