TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance

• URL: http://arxiv.org/abs/2403.06642v2
• Date: Fri, 24 May 2024 09:09:35 GMT
• Title: TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
• Authors: Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng,
• Abstract summary: A promising approach involves leveraging external knowledge to enrich behavioral-based recommender systems with abundant semantic information. This approach faces two primary challenges: denoising raw external knowledge and adapting semantic representations. We propose an External Knowledge-Enhanced Recommendation method with LLM Assistance.
• Score: 9.017820815622828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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