# Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems

arXiv: https://arxiv.org/abs/2506.11421 · HTML: https://mlsanity.com/paper/2506.11421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics](https://mlsanity.com/paper/2404.16296/) (2024)
- [Enhanced Recommendation Combining Collaborative Filtering and Large Language Models](https://mlsanity.com/paper/2412.18713/) (2024)
- [Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing](https://mlsanity.com/paper/2505.11743/) (2025)
- [Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction](https://mlsanity.com/paper/2412.05816/) (2024)
- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [Contextual Bandits for Unbounded Context Distributions](https://mlsanity.com/paper/2408.09655/) (2024)
- [Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning](https://mlsanity.com/paper/2411.15721/) (2024)
- [Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework](https://mlsanity.com/paper/2504.20442/) (2025)
- [Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising](https://mlsanity.com/paper/2505.04665/) (2025)
- [CCi-YOLOv8n: Enhanced Fire Detection with CARAFE and Context-Guided Modules](https://mlsanity.com/paper/2411.11011/) (2024)
- [Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints](https://mlsanity.com/paper/2504.05339/) (2025)
- [II-NVM: Enhancing Map Accuracy and Consistency with Normal Vector-Assisted Mapping](https://mlsanity.com/paper/2504.08204/) (2025)
- [Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture](https://mlsanity.com/paper/2505.09907/) (2025)
- [CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction](https://mlsanity.com/paper/2505.12203/) (2025)
- [A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field](https://mlsanity.com/paper/2501.04996/) (2025)
- [Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning](https://mlsanity.com/paper/2503.07340/) (2025)
- [Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion](https://mlsanity.com/paper/2504.18631/) (2025)
- [Research on feature fusion and multimodal patent text based on graph attention network](https://mlsanity.com/paper/2505.20188/) (2025)
- [Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2411.18314/) (2024)
- [Automated Parking Trajectory Generation Using Deep Reinforcement Learning](https://mlsanity.com/paper/2504.21071/) (2025)
- [Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning](https://mlsanity.com/paper/2505.19522/) (2025)

## Built on by (most cited first, 3 of 3)

- [Efficient Cold-Start Recommendation via BPE Token-Level Embedding Initialization with LLM](https://mlsanity.com/paper/2509.13179/) (2025)
- [Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users](https://mlsanity.com/paper/2509.09066/) (2025)
- [CRAMER: Control via Request-Aware Masking for Editing Recommenders](https://mlsanity.com/paper/2608.25370/) (2026)

## Abstract (arXiv)

With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined set of modeling- and system-level acceleration and optimization strategies. At the model level, we dramatically reduce parameter counts and compute requirements through lightweight network design, structured pruning, and weight quantization. At the system level, we integrate multiple heterogeneous compute platforms and high-performance inference libraries, and we design elastic inference scheduling and load-balancing mechanisms based on real-time load characteristics. Experiments show that, while maintaining the original recommendation accuracy, our methods cut latency to less than 30% of the baseline and more than double system throughput, offering a practical solution for deploying large-scale online recommendation services.