LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917877852930048 |
|---|---|
| author | Zhang, Wenlin Wu, Chuhan Li, Xiangyang Wang, Yuhao Dong, Kuicai Wang, Yichao Dai, Xinyi Zhao, Xiangyu Guo, Huifeng Tang, Ruiming |
| author_facet | Zhang, Wenlin Wu, Chuhan Li, Xiangyang Wang, Yuhao Dong, Kuicai Wang, Yichao Dai, Xinyi Zhao, Xiangyu Guo, Huifeng Tang, Ruiming |
| contents | 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 A/B testing on Huawei industrial systems. Consequently, LLMTreeRec demonstrates its effectiveness as an industry-friendly solution that has been successfully deployed online. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/LLMTreeRec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations Zhang, Wenlin Wu, Chuhan Li, Xiangyang Wang, Yuhao Dong, Kuicai Wang, Yichao Dai, Xinyi Zhao, Xiangyu Guo, Huifeng Tang, Ruiming Information Retrieval 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 A/B testing on Huawei industrial systems. Consequently, LLMTreeRec demonstrates its effectiveness as an industry-friendly solution that has been successfully deployed online. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/LLMTreeRec. |
| title | LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2404.00702 |