UniLLMRec: An End-to-End LLM-Centered Recommendation Framework to Execute Multi-Stage Recommendation Tasks Through Chain-of-Recommendations

The goal of recommender systems is to predict user preferences based on historical data. Mainly, they are designed in sequential pipelines and require lots of data to train different sub-systems, making it hard to scale to new domains. Recently, Large Language Models (LLMs)  such as ChatGPT and Claude have demonstrated remarkable generalized capabilities, enabling a […]

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