Item-to-Item (I2I) recommendation is an important function in most recommendation systems, which generates replacement or complement suggestions for a particular item based on its semantic similarities to other cataloged items. Given that subsets of items in a recommendation system might be co-interacted with by the same set of customers, graph-based models, such as graph neural networks (GNNs), provide a natural framework to combine, ingest and extract valuable insights from such high-order relational interactions between cataloged items, as well as their metadata features, as has been shown in many recent studies. However, learning GNNs effectively for I2I requires ingesting a large amount of relational data, which might not always be available, especially in new, emerging market segments. To mitigate this data bottleneck, we postulate that recommendation patterns learned from existing mature market segments (with private data) could be adapted to build effective warm-start models for emerging ones. To achieve this, we propose and investigate a personalized federated modeling framework based on GNNs to summarize, assemble and adapt recommendation patterns across market segments with heterogeneous customer behaviors into effective local models. Our key contribution is a personalized graph adaptation model that bridges the gap between recent literature on federated GNNs and (non-graph) personalized federated learning, which either does not optimize for the adaptability of the federated model or is restricted to local models with homogeneous parameterization, excluding GNNs with heterogeneous local graphs.

翻译：物品到物品（I2I）推荐是大多数推荐系统中的重要功能，它基于某物品与其他目录物品的语义相似性，生成该物品的替代或补充建议。鉴于推荐系统中的物品子集可能被同一组客户共同交互，图模型（如图神经网络，GNNs）为整合、融合并从目录物品之间及其元数据特征的高阶关系交互中提取有价值见解提供了天然框架，这一点在近期多项研究中已得到证实。然而，有效学习用于I2I推荐的GNNs需要摄入大量关系数据，而这些数据可能并非总是可用，尤其是在新兴市场细分领域。为缓解这一数据瓶颈，我们假设从成熟市场细分领域（含私有数据）学习到的推荐模式可被适应，以构建面向新兴领域的有效热启动模型。为实现此目标，我们提出并研究了一种基于GNNs的个性化联邦建模框架，用于跨具有异质客户行为的市场细分领域对推荐模式进行总结、整合与适应，从而构建有效的局部模型。我们的关键贡献在于一种个性化图适应模型，该模型弥合了近期联邦GNNs文献与（非图）个性化联邦学习之间的鸿沟——前者未优化联邦模型的可适应性，后者受限于同构参数化的局部模型且排除了具有异质局部图的GNNs。