# When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

arXiv: https://arxiv.org/abs/2305.12650 · HTML: https://mlsanity.com/paper/2305.12650/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2023.

## Built on by (most cited first, 11 of 11)

- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)
- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Federated Continual Recommendation](https://mlsanity.com/paper/2508.04792/) (2025)
- [Graph Federated Learning for Personalized Privacy Recommendation](https://mlsanity.com/paper/2508.06208/) (2025)
- [A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2506.11563/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation](https://mlsanity.com/paper/2512.24715/) (2025)
- [SOLAR: Communication-Efficient Model Adaptation via Subspace-Oriented Latent Adapter Reparametrization](https://mlsanity.com/paper/2604.08368/) (2026)
- [Personalized and Multi-View Representation for Federated Cold-Start Recommendation](https://mlsanity.com/paper/2608.27826/) (2026)

## Abstract (arXiv)

Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first research work in federated recommendation to specifically study the cold-start scenario. The proposed method learns two sets of item representations by leveraging item attributes and interaction records simultaneously. Additionally, an item representation alignment mechanism is designed to align two item representations and learn the meta attribute network at the server within a federated learning framework. Experiments on four benchmark datasets demonstrate IFedRec's superior performance for cold-start scenarios. Furthermore, we also verify IFedRec owns good robustness when the system faces limited client participation and noise injection, which brings promising practical application potential in privacy-protection enhanced federated recommendation systems. The implementation code is available