Tired Of Plugins? Large Language Models Can Be End-to-end Recommenders
Zhang Wenlin, Wu Chuhan, Li Xiangyang, Wang Yuhao, Dong Kuicai, Wang Yichao, Dai Xinyi, Zhao Xiangyu, Guo Huifeng, Tang Ruiming. Arxiv 2024
[Paper]
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.
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