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

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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 singular model to tackle diverse recommendation tasks across various scenarios. However,  these systems face challenges in presenting large-scale item sets to LLMs in natural language format due to the constraint of input length. In prior research, recommendation tasks have been approached within the natural language

This is a companion discussion topic for the original entry at https://www.marktechpost.com/2024/04/04/unillmrec-an-end-to-end-llm-centered-recommendation-framework-to-execute-multi-stage-recommendation-tasks-through-chain-of-recommendations/