MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation

• URL: http://arxiv.org/abs/2601.20234v2
• Date: Thu, 29 Jan 2026 04:56:16 GMT
• Title: MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
• Authors: Qihang Yu, Kairui Fu, Zhaocheng Du, Yuxuan Si, Kaiyuan Li, Weihao Zhao, Zhicheng Zhang, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Shengyu Zhang, Kun Kuang, Fei Wu,
• Abstract summary: MALLOC is a benchmark for memory-aware long sequence compression.<n>It is integrated into state-of-the-art recommenders, enabling a reproducible and evaluation platform.
• Score: 84.53415999381203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both industry and academia. However, the advent of large-scale recommenders also brings significantly higher computational costs, particularly under the long-sequence dependencies inherent in the user intent of recommendation systems. Current approaches often rely on pre-storing the intermediate states of the past behavior for each user, thereby reducing the quadratic re-computation cost for the following requests. Despite their effectiveness, these methods often treat memory merely as a medium for acceleration, without adequately considering the space overhead it introduces. This presents a critical challenge in real-world recommendation systems with billions of users, each of whom might initiate thousands of interactions and require massive memory for state storage. Fortunately, there have been several memory management strategies examined for compression in LLM, while most have not been evaluated on the recommendation task. To mitigate this gap, we introduce MALLOC, a comprehensive benchmark for memory-aware long sequence compression. MALLOC presents a comprehensive investigation and systematic classification of memory management techniques applicable to large sequential recommendations. These techniques are integrated into state-of-the-art recommenders, enabling a reproducible and accessible evaluation platform. Through extensive experiments across accuracy, efficiency, and complexity, we demonstrate the holistic reliability of MALLOC in advancing large-scale recommendation. Code is available at https://anonymous.4open.science/r/MALLOC.

Related papers

• UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory [46.87954895079213]
  Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)<n>Existing methods predominately optimize memory management while treating memory extraction as a static process.<n>We propose Unified Memory Extraction and Management (UMEM) to jointly optimize a Large Language Model to simultaneous extract and manage memories.
  arXiv  Detail & Related papers  (2026-02-11T08:58:41Z)
• MSN: A Memory-based Sparse Activation Scaling Framework for Large-scale Industrial Recommendation [19.132874291460936]
  We propose MSN, a memory-based sparse activation scaling framework for recommendation models.<n> MSN retrieves personalized representations from a large parameterized memory and integrates them into downstream feature interaction modules.<n> MSN consistently improves recommendation performance while maintaining high efficiency.
  arXiv  Detail & Related papers  (2026-02-07T12:43:51Z)
• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• A Universal Framework for Compressing Embeddings in CTR Prediction [68.27582084015044]
  We introduce a Model-agnostic Embedding Compression (MEC) framework that compresses embedding tables by quantizing pre-trained embeddings.<n>Our approach consists of two stages: first, we apply popularity-weighted regularization to balance code distribution between high- and low-frequency features.<n> Experiments on three datasets reveal that our method reduces memory usage by over 50x while maintaining or improving recommendation performance.
  arXiv  Detail & Related papers  (2025-02-21T10:12:34Z)
• Large Memory Network for Recommendation [21.618829330517844]
  Large Memory Network (LMN) is a novel idea by compressing and storing user history behavior information in a large-scale memory block.<n>LMN has been fully deployed in Douyin E-Commerce Search (ECS), serving millions of users each day.
  arXiv  Detail & Related papers  (2025-02-08T13:08:11Z)
• Continuous Input Embedding Size Search For Recommender Systems [60.89189829112067]
  Continuous input embedding size search (CIESS) is a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from.<n> CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs.<n> experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets.
  arXiv  Detail & Related papers  (2023-04-07T06:46:37Z)
• A Generic Network Compression Framework for Sequential Recommender Systems [71.81962915192022]
  Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. We propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed. By the extensive ablation studies, we demonstrate that the proposed CpRec can achieve up to 4$sim$8 times compression rates in real-world SRS datasets.
  arXiv  Detail & Related papers  (2020-04-21T08:40:55Z)

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.