# Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy

arXiv: https://arxiv.org/abs/2504.08751 · HTML: https://mlsanity.com/paper/2504.08751/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 27 of 27)

- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts](https://mlsanity.com/paper/2405.11613/) (2024)
- [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)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning](https://mlsanity.com/paper/2408.03084/) (2024)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models](https://mlsanity.com/paper/2501.07033/) (2025)
- [Twitter Sentiment Analysis of Covid Vacciness](https://mlsanity.com/paper/2311.04479/) (2023)
- [A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model](https://mlsanity.com/paper/2407.08942/) (2024)
- [Machine Learning-Based Research on the Adaptability of Adolescents to Online Education](https://mlsanity.com/paper/2408.16849/) (2024)
- [DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment](https://mlsanity.com/paper/2409.03930/) (2024)
- [Optimizing Distributed Networking with Big Data Scheduling and Cloud Computing](https://mlsanity.com/paper/2311.04482/) (2023)
- [Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data](https://mlsanity.com/paper/2410.14963/) (2024)
- [Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction](https://mlsanity.com/paper/2412.05816/) (2024)
- [Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning](https://mlsanity.com/paper/2411.15721/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient](https://mlsanity.com/paper/2411.13885/) (2024)
- [Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model](https://mlsanity.com/paper/2412.07997/) (2024)
- [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)
- [A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm](https://mlsanity.com/paper/2412.07223/) (2024)
- [Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model](https://mlsanity.com/paper/2410.09923/) (2024)
- [Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models](https://mlsanity.com/paper/2410.19130/) (2024)
- [Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning](https://mlsanity.com/paper/2409.02425/) (2024)
- [Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms](https://mlsanity.com/paper/2410.19394/) (2024)
- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)

## Built on by (most cited first, 9 of 9)

- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2506.17551/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders](https://mlsanity.com/paper/2508.05289/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
- [LLM-Augmented Symptom Analysis for Cardiovascular Disease Risk Prediction: A Clinical NLP](https://mlsanity.com/paper/2507.11052/) (2025)
- [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)
- [Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms](https://mlsanity.com/paper/2509.04751/) (2025)

## Abstract (arXiv)

With the rapid development of short video platforms, recommendation systems have become key technologies for improving user experience and enhancing platform engagement. However, while short video recommendation systems leverage multimodal information (such as images, text, and audio) to improve recommendation effectiveness, they also face the severe challenge of user privacy leakage. This paper proposes a short video recommendation system based on multimodal information and differential privacy protection. First, deep learning models are used for feature extraction and fusion of multimodal data, effectively improving recommendation accuracy. Then, a differential privacy protection mechanism suitable for recommendation scenarios is designed to ensure user data privacy while maintaining system performance. Experimental results show that the proposed method outperforms existing mainstream approaches in terms of recommendation accuracy, multimodal fusion effectiveness, and privacy protection performance, providing important insights for the design of recommendation systems for short video platforms.