  Combining semantic information with behavioral data is a crucial research
area in recommender systems. A promising approach involves leveraging external
knowledge to enrich behavioral-based recommender systems with abundant semantic
information. However, this approach faces two primary challenges: denoising raw
external knowledge and adapting semantic representations. To address these
challenges, we propose an External Knowledge-Enhanced Recommendation method
with LLM Assistance (TRAWL). This method utilizes large language models (LLMs)
to extract relevant recommendation knowledge from raw external data and employs
a contrastive learning strategy for adapter training. Experiments on public
datasets and real-world online recommender systems validate the effectiveness
of our approach.
