Federated recommendation system is a recently emerging architecture, which provides recommendation services without exposing users' private data. Existing methods are mainly designed to recommend items already existing in the system. In practical scenarios, the system continuously introduces new items and recommends them to users, i.e., cold-start recommendation. To recommend cold items, existing federated recommendation models require collecting new interactions from users and retraining the model, which is time-consuming and poses a privacy threat to users' sensitive information. This paper presents a novel Item-guided Federated aggregation for cold-start Recommendation (IFedRec) framework. The IFedRec exchanges the item embedding to learn the common item preference semantic and preserves other model parameters locally to capture user personalization. Besides, it deploys a meta attribute network on the server to learn the item feature semantic, and a semantic alignment mechanism is presented to align both kinds of item semantic. When the new items arrive, each client can make recommendations with item feature semantic learned from the meta attribute network by incorporating the locally personalized model without retraining. Experiments on four benchmark datasets demonstrate IFedRec's outstanding performance for cold-start recommendation. Besides, in-depth analysis verifies IFedRec's learning ability for cold items while protecting user's privacy.

翻译：联邦推荐系统是一种新兴架构，能在不暴露用户隐私数据的前提下提供推荐服务。现有方法主要面向系统中已存在的物品进行推荐。在实际场景中，系统会持续引入新物品并向用户推荐，即冷启动推荐。为推荐冷物品，现有联邦推荐模型需要收集用户的新交互数据并重新训练模型，这不仅耗时，还存在泄露用户敏感信息的隐私风险。本文提出一种新颖的面向冷启动推荐的物导向联邦聚合框架（IFedRec）。IFedRec通过交换物品嵌入来学习通用物品偏好语义，并将其他模型参数本地保留以捕捉用户个性化特征。此外，该方法在服务器端部署元属性网络以学习物品特征语义，并设计语义对齐机制来对齐两类物品语义。当新物品出现时，每个客户端无需重新训练，即可利用元属性网络学到的物品特征语义，结合本地个性化模型进行推荐。在四个基准数据集上的实验表明，IFedRec在冷启动推荐中具有卓越性能。同时，深入分析验证了IFedRec在保护用户隐私的前提下对冷物品的学习能力。