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arXiv Machine Learning By Ziliang Zhao, Bi Xue, Emma Lin, Tianqi Lu, Mengjiao Zhou, Kaustubh Vartak, Shakhzod Ali-Zade, Tao Li, Bin Kuang, Rui Jian, Bin Wen, Dennis van der Staay, Yixin Bao, Xiujin Li, Chao Deng, Henry Wei, Songbin Liu, Qifan Wang, Kai Ren

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders

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The paper introduces Multi-Probe Zero Collision Hash (MPZCH), an indexing mechanism that uses linear probing to reduce or eliminate collisions in embedding tables for large-scale recommendation systems. MPZCH employs auxiliary tensors and CUDA kernels to support configurable probing and active eviction, thereby retiring obsolete IDs and resetting reassigned slots to prevent stale embeddings. Experiments show that MPZCH achieves zero collisions for user embeddings, improves item embedding freshness and quality, and maintains training and inference performance comparable to existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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