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

Source: [When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions](<https://www.sota2.com/research/paper/68fa72581e209131cea55cea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:04.665Z

Authors: Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang

Published: 2023-05-22T02:51:27.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Federated Recommendation on KU](<https://www.sota2.com/research/sota/federated-recommendation-on-ku.md>): Federated Recommendation; KU; Recall: 22.84.

- [Federated Recommendation on Movie](<https://www.sota2.com/research/sota/federated-recommendation-on-movie.md>): Federated Recommendation; Movie; Recall: 19.81.

- [Federated Recommendation on Dance](<https://www.sota2.com/research/sota/federated-recommendation-on-dance.md>): Federated Recommendation; Dance; Recall: 31.73.

- [Federated Recommendation on Food](<https://www.sota2.com/research/sota/federated-recommendation-on-food.md>): Federated Recommendation; Food; Recall: 32.25.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.12650>)

[PDF](<https://arxiv.org/pdf/2305.12650.pdf>)