Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
<|reference_start|>Tired of Plugins? Large Language Models Can Be End-To-End Recommenders: 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.<|reference_end|>
@article{zhang2024tired,
    title={Tired of Plugins? Large Language Models Can Be End-To-End Recommenders},
    author={Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao
  Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang},
    journal={arXiv preprint arXiv:2404.00702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00702},
    primaryClass={cs.IR}
}