  Recommender systems aim to predict user interest based on historical
behavioral data. They are mainly designed in sequential pipelines, requiring
lots of data to train different sub-systems, and are hard to scale to new
domains. Recently, Large Language Models (LLMs) have demonstrated remarkable
generalized capabilities, enabling a singular model to tackle diverse
recommendation tasks across various scenarios. Nonetheless, existing LLM-based
recommendation systems utilize LLM purely for a single task of the
recommendation pipeline. Besides, these systems face challenges in presenting
large-scale item sets to LLMs in natural language format, due to the constraint
of input length. To address these challenges, we introduce an LLM-based
end-to-end recommendation framework: UniLLMRec. Specifically, UniLLMRec
integrates multi-stage tasks (e.g. recall, ranking, re-ranking) via
chain-of-recommendations. To deal with large-scale items, we propose a novel
strategy to structure all items into an item tree, which can be dynamically
updated and effectively retrieved. UniLLMRec shows promising zero-shot results
in comparison with conventional supervised models. Additionally, it boasts high
efficiency, reducing the input token need by 86% compared to existing LLM-based
models. Such efficiency not only accelerates task completion but also optimizes
resource utilization. To facilitate model understanding and to ensure
reproducibility, we have made our code publicly available.
