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MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity

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arxiv 2411.09425 v3 pith:ORFT6NAY submitted 2024-11-14 cs.IR

Xiao Lv , Jiangxia Cao , Shijie Guan , Xiaoyou Zhou , Zhiguang Qi , Yaqiang Zang , Ming Li , Ben Wang
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Kun Gai Guorui Zhou
This is my paper · ORCID
classification cs.IR
keywords modelrecommendationcomplexityparametersamountbillioncomputationalcontrol
verification ladder T0 review T1 audit T2 compute T3 formal
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Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system can generate over 50 billion user samples daily, and such a massive amount of training data can easily allow our model parameters to exceed 200 billion, surpassing many LLMs (about 100B). (2) To ensure the stability and robustness of the recommendation system, it is essential to control computational complexity FLOPs carefully. Considering the above differences with LLM, we can draw a conclusion that: for a RecSys model, compared to model parameters, the computational complexity FLOPs is a more expensive factor that requires careful control. In this paper, we propose our milestone work, MARM (Memory Augmented Recommendation Model), which explores a new cache scaling-laws successfully.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CAPTS: Channel-Aware, Preference-Aligned Trigger Selection for Multi-Channel Item-to-Item Retrieval

    cs.IR 2026-02 conditional novelty 6.0 of 10

    CAPTS selects retrieval triggers by predicted downstream engagement per I2I channel and routes them jointly, reporting offline Recall@K gains and a small online time-spent lift.

  2. Request-Only Optimization for Recommendation Systems

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A request-level training data format eliminates duplicate user features, increasing storage efficiency and training throughput while enabling larger recommendation architectures.

  3. A Contextual-Aware Position Encoding for Sequential Recommendation

    cs.IR 2025-02 conditional novelty 5.0 of 10

    CAPE computes context-dependent positions from item dissimilarity and fuses them into attention, improving sequential recommenders on benchmark and live-traffic tests.

  4. Climber: Toward Efficient Scaling Laws for Large Recommendation Models

    cs.IR 2025-02 conditional novelty 4.0 of 10

    Climber reports that splitting user sequences by behavior type, adding adaptive temperature, and co-designed batching enable more efficient Transformer scaling in recommender systems.

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