Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
<|reference_start|>Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application: Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance in cold-start scenarios and long-tail user recommendations. Leveraging the capabilities of Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems by integrating open-world domain knowledge. In this paper, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through offline experiments on the large-scale industrial dataset and online experiments on A/B tests, we demonstrate the efficacy of our approach.<|reference_end|>
@article{jia2024knowledge,
    title={Knowledge Adaptation from Large Language Model to Recommendation for
  Practical Industrial Application},
    author={Jian Jia, Yipei Wang, Yan Li, Honggang Chen, Xuehan Bai, Zhaocheng
  Liu, Jian Liang, Quan Chen, Han Li, Peng Jiang, Kun Gai},
    journal={arXiv preprint arXiv:2405.03988},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03988},
    primaryClass={cs.IR cs.AI}
}