# Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models

arXiv: https://arxiv.org/abs/2412.18715 · HTML: https://mlsanity.com/paper/2412.18715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving](https://mlsanity.com/paper/2409.03320/) (2024)
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- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (2024)
- [Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics](https://mlsanity.com/paper/2409.08530/) (2024)
- [Research on Heterogeneous Computation Resource Allocation based on Data-driven Method](https://mlsanity.com/paper/2408.05671/) (2024)
- [Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning](https://mlsanity.com/paper/2409.02425/) (2024)
- [An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX)](https://mlsanity.com/paper/2409.15315/) (2024)
- [Artistic Neural Style Transfer Algorithms with Activation Smoothing](https://mlsanity.com/paper/2411.08014/) (2024)
- [Ventilator pressure prediction using recurrent neural network](https://mlsanity.com/paper/2410.06552/) (2024)
- [Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition](https://mlsanity.com/paper/2409.12376/) (2024)

## Built on by (most cited first, 1 of 1)

- [Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving](https://mlsanity.com/paper/2501.06680/) (2025)

## Abstract (arXiv)

With the rapid development of large language models (LLMs) and the growing demand for personalized content, recommendation systems have become critical in enhancing user experience and driving engagement. Collaborative filtering algorithms, being core to many recommendation systems, have garnered significant attention for their efficiency and interpretability. However, traditional collaborative filtering approaches face numerous challenges when integrated into large-scale LLM-based systems, including high computational costs, severe data sparsity, cold start problems, and lack of scalability. This paper investigates the optimization and scalability of collaborative filtering algorithms in large language models, addressing these limitations through advanced optimization strategies. Firstly, we analyze the fundamental principles of collaborative filtering algorithms and their limitations when applied in LLM-based contexts. Next, several optimization techniques such as matrix factorization, approximate nearest neighbor search, and parallel computing are proposed to enhance computational efficiency and model accuracy. Additionally, strategies such as distributed architecture and model compression are explored to facilitate dynamic updates and scalability in data-intensive environments.