[Paper Review] Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models

Haowei Yang, Longfei Yun+3 more|arXiv (Cornell University)|Dec 25, 2024

Recommender Systems and Techniques4 citations

TL;DR

This paper proposes optimized collaborative filtering (CF) techniques—such as matrix factorization, approximate nearest neighbor search, and distributed parallel computing—for integration into large language models (LLMs), significantly improving computational efficiency and scalability. It demonstrates that these methods reduce inference latency by up to 60% and enhance recommendation accuracy under data sparsity and cold start conditions.

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.

Motivation & Objective

• To address the scalability and computational inefficiency of traditional collaborative filtering in large language model (LLM)-based recommendation systems.
• To mitigate data sparsity and cold start problems in LLM-integrated recommendation pipelines.
• To enhance model accuracy and update frequency through distributed architecture and model compression techniques.
• To enable real-time, dynamic recommendations in data-intensive environments using optimized CF algorithms.
• To establish a framework for integrating collaborative filtering with LLMs that balances interpretability, efficiency, and scalability.

Proposed method

• Application of matrix factorization to reduce dimensionality and improve latent representation learning in user-item interaction spaces.
• Integration of approximate nearest neighbor (ANN) search to accelerate item retrieval and reduce inference latency.
• Employment of parallel and distributed computing frameworks to scale CF across large-scale user and item sets.
• Adoption of model compression techniques to reduce model size and enable faster inference without significant accuracy loss.
• Design of a distributed system architecture that supports incremental updates and dynamic recommendation serving.
• Combination of LLM-generated embeddings with CF-based collaborative signals to enhance personalization while preserving interpretability.

Experimental results

Research questions

• RQ1 How can collaborative filtering be optimized to reduce computational cost in LLM-based recommendation systems?
• RQ2 To what extent can approximate nearest neighbor search improve inference speed in large-scale CF systems?
• RQ3 How does distributed architecture impact the scalability and update frequency of CF models in data-intensive LLM environments?
• RQ4 What trade-offs exist between model compression and recommendation accuracy in hybrid LLM-CF systems?
• RQ5 Can the integration of LLMs with collaborative filtering effectively alleviate data sparsity and cold start issues?

Key findings

• Matrix factorization reduced model inference latency by up to 60% in large-scale benchmarks compared to standard CF approaches.
• Approximate nearest neighbor search achieved near-identical recommendation accuracy to exact search while reducing retrieval time by 70%.
• Distributed computing enabled the system to scale to over 10 million user-item interactions with linear performance growth.
• Model compression techniques reduced model size by 55% with less than 2% drop in recommendation accuracy on benchmark datasets.
• The hybrid LLM-CF framework improved cold start performance by 40% on new users and items compared to standalone CF or LLM-only methods.
• The proposed system maintained high accuracy under extreme data sparsity, outperforming baseline CF models by 25% in low-density user-item matrices.