In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience. While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions. Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN). This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism. We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task. This strategy effectively reduces the risk of overfitting when dealing with limited data. Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.

翻译：在推荐系统中，新物品会持续引入，初始阶段缺乏交互记录，但会随时间逐渐积累。准确预测这些物品的点击率对于提升收益和用户体验至关重要。现有方法主要关注在通用点击率预测模型中增强新物品的ID嵌入，但往往采用全局特征交互方式，容易使数据稀疏的新物品被交互丰富的物品所掩盖。针对这一问题，本文提出EmerG，一种通过学习物品特定特征交互模式来预热冷启动点击率预测的新方法。EmerG利用超网络根据物品特征生成物品特定的特征图，随后通过图神经网络进行处理。该图神经网络经过专门设计，通过定制的消息传递机制，可证明能够捕捉任意阶的特征交互。我们进一步设计了一种元学习策略，在多个物品点击率预测任务中优化超网络和图神经网络的参数，同时在每个任务中仅调整最小化的物品特定参数集。该策略有效降低了处理有限数据时的过拟合风险。在基准数据集上的大量实验验证了EmerG在新物品实例为零、少量和充足的情况下均能持续取得最佳性能。