AI RESEARCH
OCP: Orthogonal Constrained Projection for Sparse Scaling in Industrial Commodity Recommendation
arXiv CS.LG
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ArXi:2603.18697v1 Announce Type: new 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.