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Monolith: Real Time Recommendation System With Collisionless Embedding Table

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arxiv 2209.07663 v2 pith:MEDKC5QW submitted 2022-09-16 cs.IR

Zhuoran Liu , Leqi Zou , Xuan Zou , Caihua Wang , Biao Zhang , Da Tang , Bolin Zhu , Yijie Zhu
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Peng Wu Ke Wang Youlong Cheng
This is my paper · ORCID
classification cs.IR
keywords recommendationsystemframeworksmonolithonlinereal-timecollisionlesscustomer
verification ladder T0 review T1 audit T2 compute T3 formal
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Building a scalable and real-time recommendation system is vital for many businesses driven by time-sensitive customer feedback, such as short-videos ranking or online ads. Despite the ubiquitous adoption of production-scale deep learning frameworks like TensorFlow or PyTorch, these general-purpose frameworks fall short of business demands in recommendation scenarios for various reasons: on one hand, tweaking systems based on static parameters and dense computations for recommendation with dynamic and sparse features is detrimental to model quality; on the other hand, such frameworks are designed with batch-training stage and serving stage completely separated, preventing the model from interacting with customer feedback in real-time. These issues led us to reexamine traditional approaches and explore radically different design choices. In this paper, we present Monolith, a system tailored for online training. Our design has been driven by observations of our application workloads and production environment that reflects a marked departure from other recommendations systems. Our contributions are manifold: first, we crafted a collisionless embedding table with optimizations such as expirable embeddings and frequency filtering to reduce its memory footprint; second, we provide an production-ready online training architecture with high fault-tolerance; finally, we proved that system reliability could be traded-off for real-time learning. Monolith has successfully landed in the BytePlus Recommend product.

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Cited by 8 Pith papers

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

  1. Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta

    cs.IR 2026-07 conditional novelty 6.0 of 10

    Mosaic shows that a fleet of four heterogeneous user-embedding specialists, trained with redundancy-reduction and composite-label losses, improves downstream recommendation quality at Meta.

  2. xGR: Efficient Generative Recommendation Serving at Scale

    cs.LG 2025-12 conditional novelty 6.0 of 10

    On real-world recommendation datasets, xGR sustains about 2.9–3.5× the throughput of vLLM/xLLM under a 200 ms P99 latency cap through GR-specific KV-cache, beam-search, and scheduling optimizations.

  3. Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest

    cs.LG 2025-09 conditional novelty 5.0 of 10

    An RL agent that picks personalized weights for a linear ad ranking utility raised treated-segment CTR by 9.7% and CTR30 by 7.7% in Pinterest's production system.

  4. Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training

    cs.IR 2025-08 conditional novelty 5.0 of 10

    Pre-training ID embeddings with contrastive loss in a simple model avoids one-epoch overfitting and improves Pinterest's recommendation engagement by 2.2%.

  5. DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation

    cs.IR 2025-06 conditional novelty 5.0 of 10

    DCN^2 augments DCNv2 with collision-weighted lookups, a dense-only cross layer, and an FFM-like similarity layer, and reports improved offline and online recommendation performance.

  6. Reinforcement Learning from User Feedback

    cs.AI 2025-05 conditional novelty 5.0 of 10

    A reward model trained on sparse heart-emoji reactions predicted online Love Reaction rates (r=0.95 across ten models) and, when added to multi-objective RL, lifted Love Reactions by up to 28% in live A/B tests, with ...

  7. LO-FAR: A Cost-Aware Local Filter for Sparse Feature Ranking in Industrial Ad Recommendation

    cs.IR 2026-07 conditional novelty 4.0 of 10

    LO-FAR ranks sparse ID-list features by stand-alone held-out predictive signal and reports downstream NE gains competitive with shuffle importance and BSN at 100–400 retained features in about two CPU-hours.

  8. Mutable Low-Rank Sketches for Retrain-Free Recommendation

    cs.LG 2026-07 conditional novelty 4.0 of 10

    A KP-tree-based mutable sketch lets user embeddings update in O(log n) per rating through a fixed basis, achieving 0.810 RMSE on KuaiRec at 1.8% data read.

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