用图神经网络融合局部与全局表示，提升推荐系统精度
ContextGNN: Beyond Two-Tower Recommendation Systems
- 结合用户局部子图的成对表示与全局双塔表示
- 在多个数据集上平均性能提升20%
- 适合需要兼顾个性化与探索性的推荐场景
推荐系统普遍采用双塔架构，通过用户与物品嵌入的内积评估排序。但双塔模型学习的是无关配对的用户和物品表示，存在局限性。成对表示虽更精准，却因二次复杂度难以扩展或对候选对过于限制。为此，我们提出基于上下文的图神经网络（ContextGNN），用于推荐系统的链接预测。该方法在用户局部子图中对熟悉物品采用成对表示，同时利用双塔表示支持探索性物品推荐。最终网络融合两类推荐结果生成统一排序。实验表明，ContextGNN能适应不同数据特征，在多种实际推荐任务中优于传统及基于GNN的方法，平均性能提升20%。
原文摘要 · Abstract (English)
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average.
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