Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models

Collaborative filtering (CF) is one of the most widely adopted algorithms in recommendation systems due to its ability to generate personalized recommendations based on user behavior data. However, the rapid growth in data volume and model complexity poses significant challenges to traditional collaborative filtering algorithms[2]. These include high computational overhead, data sparsity, the cold start problem, and difficulty in scaling.In the context of LLM-based recommendation systems, these challensges are further amplified due to the intricate interactions between users, content, and language model parameters. This research explores the optimization and scalability of collaborative filtering algorithms within large language models. We propose several optimization strategies, including matrix factorization, approximate nearest neighbor search, and parallel computing, to reduce computational complexity and improve accuracy[3].This work builds on insights from [4], particularly its integration of neural matrix factorization with large language models to address cold start issues and improve recommendation accuracy through multimodal data.The multimodal fusion strategies and transformer-based methods in [5] provide valuable insights for improving data integration and scalability in collaborative filtering algorithms.The key insight from [6] is their approach to handling data imbalance and scalability, which is highly relevant for optimizing collaborative filtering algorithms in large language model-based recommendation systems.The use of CNNs and LSTMs in [7] for capturing nonlinear patterns informs optimizing collaborative filtering algorithms in LLM-based systems, improving efficiency and accuracy.