  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.
