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

TL;DR

This paper proposes IFedRec, a federated recommendation method that effectively addresses cold-start problems by learning aligned item representations from attributes and interactions, showing superior performance and robustness in experiments.

Contribution

It introduces a novel federated learning approach that separates and aligns item attribute and interaction representations for cold-start recommendation.

Findings

IFedRec outperforms existing methods in cold-start scenarios.

The method demonstrates robustness with limited client participation.

It maintains high performance despite noise injection.

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