TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
This work addresses the need for more accurate and semantically rich recommendations in online systems, but it appears incremental as it builds on existing approaches with LLM assistance.
The paper tackled the problem of enhancing recommender systems by integrating external knowledge, addressing challenges in denoising raw data and adapting semantic representations, and achieved validated effectiveness through experiments on public datasets and real-world systems.
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.