@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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
%A Zhang, Wenlin
%A Wu, Chuhan
%A Li, Xiangyang
%A Wang, Yuhao
%A Dong, Kuicai
%A Wang, Yichao
%A Dai, Xinyi
%A Zhao, Xiangyu
%A Guo, Huifeng
%A Tang, Ruiming
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-etal-2025-llmtreerec
%X 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.
%U https://aclanthology.org/2025.coling-main.59/
%P 886-896
Markdown (Informal)
[LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations](https://aclanthology.org/2025.coling-main.59/) (Zhang et al., COLING 2025)
ACL
- Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, and Ruiming Tang. 2025. LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations. In Proceedings of the 31st International Conference on Computational Linguistics, pages 886–896, Abu Dhabi, UAE. Association for Computational Linguistics.