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

• URL: http://arxiv.org/abs/2602.17050v2
• Date: Mon, 23 Feb 2026 21:14:23 GMT
• Title: Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders
• Authors: Ziliang Zhao, Bi Xue, Emma Lin, Mengjiao Zhou, Kaustubh Vartak, Shakhzod Ali-Zade, Tianqi Lu, Tao Li, Bin Kuang, Rui Jian, Bin Wen, Dennis van der Staay, Yixin Bao, Eddy Li, Chao Deng, Songbin Liu, Qifan Wang, Kai Ren,
• Abstract summary: Multi-Probe Zero Collision Hash (MPZCH) is a novel indexing mechanism based on linear probing.<n>MPZCH achieves zero collisions for user embeddings and significantly improves item embedding freshness and quality.<n>The solution has been released within the open-source TorchRec library for the broader community.
• Score: 47.3074050788206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embedding tables are critical components of large-scale recommendation systems, facilitating the efficient mapping of high-cardinality categorical features into dense vector representations. However, as the volume of unique IDs expands, traditional hash-based indexing methods suffer from collisions that degrade model performance and personalization quality. We present Multi-Probe Zero Collision Hash (MPZCH), a novel indexing mechanism based on linear probing that effectively mitigates embedding collisions. With reasonable table sizing, it often eliminates these collisions entirely while maintaining production-scale efficiency. MPZCH utilizes auxiliary tensors and high-performance CUDA kernels to implement configurable probing and active eviction policies. By retiring obsolete IDs and resetting reassigned slots, MPZCH prevents the stale embedding inheritance typical of hash-based methods, ensuring new features learn effectively from scratch. Despite its collision-mitigation overhead, the system maintains training QPS and inference latency comparable to existing methods. Rigorous online experiments demonstrate that MPZCH achieves zero collisions for user embeddings and significantly improves item embedding freshness and quality. The solution has been released within the open-source TorchRec library for the broader community.

Related papers

• KVCompose: Efficient Structured KV Cache Compression with Composite Tokens [7.922206020386125]
  Large language models (LLMs) rely on key-value (KV) caches for efficient autoregressive decoding.<n>We propose a simple, yet effective, KV cache compression framework based on attention-guided, layer-adaptive composite tokens.<n>Our method achieves significant memory reduction while preserving accuracy, consistently outperforming prior structured and semi-structured methods.
  arXiv  Detail & Related papers  (2025-09-05T14:58:24Z)
• Exploiting Discriminative Codebook Prior for Autoregressive Image Generation [54.14166700058777]
  token-based autoregressive image generation systems first tokenize images into sequences of token indices with a codebook, and then model these sequences in an autoregressive paradigm.<n>While autoregressive generative models are trained only on index values, the prior encoded in the codebook, which contains rich token similarity information, is not exploited.<n>Recent studies have attempted to incorporate this prior by performing naive k-means clustering on the tokens, helping to facilitate the training of generative models with a reduced codebook.<n>We propose the Discriminative Codebook Prior Extractor (DCPE) as an alternative to k-means
  arXiv  Detail & Related papers  (2025-08-14T15:00:00Z)
• Collision Avoidance Verification of Multiagent Systems with Learned Policies [9.550601011551024]
  This paper presents a backward reachability-based approach for verifying the collision avoidance properties of Multi-Agent Feedback Loops (MA-NFLs) We account for many uncertainties, making it well aligned with real-world scenarios. We demonstrate the proposed algorithm can verify collision-free properties of a MA-NFL with agents trained to imitate a collision avoidance algorithm.
  arXiv  Detail & Related papers  (2024-03-05T20:36:26Z)
• Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing [11.885388917784804]
  We develop one of the first private, personalized, and memory-efficient on-device LSH frameworks. Our framework enables privacy and personalization by allowing each device to generate hash tables, without the help of a central host. We prove several statistical and sensitivity properties of our hash functions, and experimentally demonstrate that our framework is competitive in training large-scale recommender networks.
  arXiv  Detail & Related papers  (2023-06-05T03:33:26Z)
• Efficient Pruning for Machine Learning Under Homomorphic Encryption [2.2817485071636376]
  Privacy-preserving machine learning (PPML) solutions are gaining widespread popularity. Many rely on homomorphic encryption (HE) that offers confidentiality of the model and the data, but at the cost of large latency and memory requirements. We introduce a framework called HE-PEx that comprises new pruning methods, on top of a packing technique called tile tensors, for reducing the latency and memory of PPML inference.
  arXiv  Detail & Related papers  (2022-07-07T15:49:24Z)
• Distributed Dynamic Safe Screening Algorithms for Sparse Regularization [73.85961005970222]
  We propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively. We prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely.
  arXiv  Detail & Related papers  (2022-04-23T02:45:55Z)
• Learning to Collide: Recommendation System Model Compression with Learned Hash Functions [4.6994057182972595]
  A key characteristic of deep recommendation models is the immense memory requirements of their embedding tables. A common technique to reduce model size is to hash all of the categorical variable identifiers (ids) into a smaller space. This hashing reduces the number of unique representations that must be stored in the embedding table; thus decreasing its size. We introduce an alternative approach, Learned Hash Functions, which instead learns a new mapping function that encourages collisions between semantically similar ids.
  arXiv  Detail & Related papers  (2022-03-28T06:07:30Z)
• Dual Cluster Contrastive learning for Person Re-Identification [78.42770787790532]
  We formulate a unified cluster contrastive framework, named Dual Cluster Contrastive learning (DCC) DCC maintains two types of memory banks: individual and centroid cluster memory banks. It can be easily applied for unsupervised or supervised person ReID.
  arXiv  Detail & Related papers  (2021-12-09T02:43:25Z)
• Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification [58.07257910065007]
  Existing approaches mainly tackle this problem by introducing attention mechanisms to locate the discriminative parts or feature encoding approaches to extract the highly parameterized features in a weakly-supervised fashion. In this work, we propose a lightweight yet effective regularization method named Channel DropBlock (CDB) in combination with two alternative correlation metrics, to address this problem.
  arXiv  Detail & Related papers  (2021-06-07T09:03:02Z)
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization [76.51980153902774]
  Federated learning (FL) is vulnerable to external attacks on FL models during parameters transmissions. In this paper, we propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
  arXiv  Detail & Related papers  (2021-01-28T03:28:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.