OCP：正交约束投影用于工业商品推荐的稀疏扩展
OCP: Orthogonal Constrained Projection for Sparse Scaling in Industrial Commodity Recommendation
AI总结：
本文提出OCP方法，通过正交约束投影优化嵌入表示，提升推荐模型的可扩展性和泛化能力，实验证明其在大规模部署中有效提升UCXR和GMV。
摘要：
在工业商品推荐系统中，物品-ID词汇表的表示质量直接影响推荐模型的可扩展性和泛化能力。传统物品-ID词汇表在稀疏扩展时面临低频信息干扰问题，限制了其对大规模物品集的表达能力，导致表示崩溃。为此，本文提出正交约束投影方法，通过强制正交性约束反向传播流形，使学习到的嵌入的奇异值谱与正交基对齐，从而保持高奇异熵，保留各向同性广义特征，同时抑制虚假相关性和对稀有物品的过拟合。实验证明，OCP加速损失收敛并提升模型可扩展性；值得注意的是，其在扩展密集层时也实现一致的性能提升。大规模工业部署在京东进一步验证了其有效性，带来UCXR提升12.97%和GMV提升8.9%，凸显其在扩展稀疏词汇表和密集架构方面的鲁棒性。
英文摘要：
In industrial commodity recommendation systems, the representation quality of Item-Id vocabularies directly impacts the scalability and generalization ability of recommendation models. A key challenge is that traditional Item-Id vocabularies, when subjected to sparse scaling, suffer from low-frequency information interference, which restricts their expressive power for massive item sets and leads to representation collapse. To address this issue, we propose an Orthogonal Constrained Projection method to optimize embedding representation. By enforcing orthogonality, the projection constrains the backpropagation manifold, aligning the singular value spectrum of the learned embeddings with the orthogonal basis. This alignment ensures high singular entropy, thereby preserving isotropic generalized features while suppressing spurious correlations and overfitting to rare items. Empirical results demonstrate that OCP accelerates loss convergence and enhances the model's scalability; notably, it enables consistent performance gains when scaling up dense layers. Large-scale industrial deployment on JD.com further confirms its efficacy, yielding a 12.97% increase in UCXR and an 8.9% uplift in GMV, highlighting its robust utility for scaling up both sparse vocabularies and dense architectures.
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