# Monolith: Real Time Recommendation System With Collisionless Embedding Table

arXiv: https://arxiv.org/abs/2209.07663 · HTML: https://mlsanity.com/paper/2209.07663/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2022.

## Built on by (most cited first, 30 of 30)

- [Wukong: Towards a Scaling Law for Large-Scale Recommendation](https://mlsanity.com/paper/2403.02545/) (2024)
- [PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations](https://mlsanity.com/paper/2510.07784/) (2025)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID](https://mlsanity.com/paper/2504.02137/) (2025)
- [Jump Starting Bandits with LLM-Generated Prior Knowledge](https://mlsanity.com/paper/2406.19317/) (2024)
- [SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems](https://mlsanity.com/paper/2406.01631/) (2024)
- [Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://mlsanity.com/paper/2307.15893/) (2023)
- [Reinforcement Learning from User Feedback](https://mlsanity.com/paper/2505.14946/) (2025)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [Martingale Posterior Neural Networks for Fast Sequential Decision Making](https://mlsanity.com/paper/2506.11898/) (2025)
- [UnifiedRL: A Reinforcement Learning Algorithm Tailored for Multi-Task Fusion in Large-Scale Recommender Systems](https://mlsanity.com/paper/2404.17589/) (2024)
- [Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems](https://mlsanity.com/paper/2405.13238/) (2024)
- [LiNR: Model Based Neural Retrieval on GPUs at LinkedIn](https://mlsanity.com/paper/2407.13218/) (2024)
- [xGR: Efficient Generative Recommendation Serving at Scale](https://mlsanity.com/paper/2512.11529/) (2025)
- [Content-based Recommendation Engine for Video Streaming Platform](https://mlsanity.com/paper/2308.08406/) (2023)
- [Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest](https://mlsanity.com/paper/2509.05292/) (2025)
- [Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates](https://mlsanity.com/paper/2512.12295/) (2025)
- [RecSys Factory: Bounding LLM Agent Autonomy to Decision Points in the Industrial Recommender Lifecycle](https://mlsanity.com/paper/2608.11241/) (2026)
- [EnhancedRL: An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Recommender Systems](https://mlsanity.com/paper/2409.11678/) (2024)
- [CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform](https://mlsanity.com/paper/2410.18087/) (2024)
- [Accelerating Recommender Model ETL with a Streaming FPGA-GPU Dataflow](https://mlsanity.com/paper/2501.12032/) (2025)
- [DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation](https://mlsanity.com/paper/2506.21624/) (2025)
- [Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems](https://mlsanity.com/paper/2511.05499/) (2025)
- [Efficient Hyperparameter Search for Non-Stationary Model Training](https://mlsanity.com/paper/2512.01258/) (2025)
- [Inference Time Feature Injection: A Lightweight Approach for Real-Time Recommendation Freshness](https://mlsanity.com/paper/2512.14734/) (2025)
- [Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders](https://mlsanity.com/paper/2602.17050/) (2026)
- [SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data](https://mlsanity.com/paper/2605.01060/) (2026)
- [A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems](https://mlsanity.com/paper/2605.16344/) (2026)
- [Mutable Low-Rank Sketches for Retrain-Free Recommendation](https://mlsanity.com/paper/2607.15242/) (2026)
- [TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information](https://mlsanity.com/paper/2608.30394/) (2026)

## Abstract (arXiv)

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.