@inproceedings{zhang-etal-2025-llmtreerec,
    title = "{LLMT}ree{R}ec: Unleashing the Power of Large Language Models for Cold-Start Recommendations",
    author = "Zhang, Wenlin  and
      Wu, Chuhan  and
      Li, Xiangyang  and
      Wang, Yuhao  and
      Dong, Kuicai  and
      Wang, Yichao  and
      Dai, Xinyi  and
      Zhao, Xiangyu  and
      Guo, Huifeng  and
      Tang, Ruiming",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.59/",
    pages = "886--896",
    abstract = "The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models(LLMs) can model recommendation tasks as language analysis tasks and provide zero-shot results based on their vast open-world knowledge. However, the large scale of the item corpus poses a challenge to LLMs, leading to substantial token consumption that makes it impractical to deploy in real-world recommendation systems. To tackle this challenge, we introduce a tree-based LLM recommendation framework LLMTreeRec, which structures all items into an item tree to improve the efficiency of LLM`s item retrieval. LLMTreeRec achieves state-of-the-art performance under the system cold-start setting in two widely used datasets, which is even competitive with conventional deep recommendation systems that use substantial training data. Furthermore, LLMTreeRec outperforms the baseline model in the A/B test on Huawei industrial system. Consequently, LLMTreeRec demonstrates its effectiveness as an industry-friendly solution that has been successfully deployed online."
}