TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
<|reference_start|>TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance: 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.<|reference_end|>
@article{luo2024trawl:,
    title={TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance},
    author={Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng},
    journal={arXiv preprint arXiv:2403.06642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06642},
    primaryClass={cs.IR cs.AI cs.CL}
}