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.

借助大型语言模型（LLMs）预训练的能力来提升推荐系统的性能，采用 LLMs 作为物品编码器，通过 LEARN 框架将开放世界知识与协同知识相结合，以解决冷启动和长尾用户推荐中的性能问题。