Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models

2024-12-25

Academic Journal of Computing & Information Science

AI Summary

To address core challenges in large language model (LLM)-driven recommendation—namely, high computational overhead, data sparsity, severe cold-start issues, and poor scalability—this paper proposes the first systematic framework that integrates matrix factorization, approximate nearest neighbor (ANN) search, and distributed model compression for collaborative filtering (CF) enhancement. The framework supports real-time incremental updates and elastic scaling, significantly improving practicality in large-scale dynamic environments. Experiments demonstrate a 5.3× speedup in inference latency, a 37% improvement in recommendation accuracy for cold-start users, and stable real-time CF modeling for billion-scale user populations. Our key innovation lies in deeply coupling lightweight model compression with efficient approximate retrieval within the CF pipeline—establishing, for the first time, a low-latency, high-accuracy, and scalable collaborative recommendation paradigm tailored to the LLM era.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Large-scale Language Models
• Collaborative Filtering
• Recommendation Systems

Innovation

• Matrix Factorization
• Distributed Computing
• Similarity Calculation