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.

翻译：将语义信息与行为数据相结合是推荐系统领域的关键研究方向。一种颇具前景的方法是利用外部知识为基于行为的推荐系统注入丰富的语义信息。然而，该方法面临两大主要挑战：原始外部知识的去噪以及语义表征的适配。为应对这些挑战，我们提出了一种基于大语言模型辅助的外部知识增强推荐方法（TRAWL）。该方法利用大语言模型从原始外部数据中提取相关的推荐知识，并采用对比学习策略进行适配器训练。在公开数据集和真实在线推荐系统上的实验验证了我们方法的有效性。