# Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

arXiv: https://arxiv.org/abs/2412.18713 · HTML: https://mlsanity.com/paper/2412.18713/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness](https://mlsanity.com/paper/2408.04585/) (2024)
- [Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics](https://mlsanity.com/paper/2404.16296/) (2024)
- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation](https://mlsanity.com/paper/2409.11703/) (2024)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model](https://mlsanity.com/paper/2407.08942/) (2024)
- [Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning](https://mlsanity.com/paper/2407.18962/) (2024)
- [Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles](https://mlsanity.com/paper/2406.04821/) (2024)
- [Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data](https://mlsanity.com/paper/2410.14963/) (2024)
- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (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)
- [Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model](https://mlsanity.com/paper/2412.07997/) (2024)
- [Mitigating Knowledge Conflicts in Language Model-Driven Question Answering](https://mlsanity.com/paper/2411.11344/) (2024)

## Built on by (most cited first, 10 of 10)

- [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)
- [Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning](https://mlsanity.com/paper/2605.15299/) (2026)

## Abstract (arXiv)

With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their effectiveness in capturing user behavior patterns, but they encounter limitations when dealing with cold start problems and data sparsity. Large Language Models (LLMs), with their strong natural language understanding and generation capabilities, provide a new breakthrough for recommendation systems. This study proposes an enhanced recommendation method that combines collaborative filtering and LLMs, aiming to leverage collaborative filtering's advantage in modeling user preferences while enhancing the understanding of textual information about users and items through LLMs to improve recommendation accuracy and diversity. This paper first introduces the fundamental theories of collaborative filtering and LLMs, then designs a recommendation system architecture that integrates both, and validates the system's effectiveness through experiments. The results show that the hybrid model based on collaborative filtering and LLMs significantly improves precision, recall, and user satisfaction, demonstrating its potential in complex recommendation scenarios.