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TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance

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arxiv 2403.06642 v2 pith:AQAXZ4LF submitted 2024-03-11 cs.IR cs.AIcs.CL

Weiqing Luo , Chonggang Song , Lingling Yi , Gong Cheng This is my paper · ORCID
classification cs.IRcs.AIcs.CL
keywords externalapproachknowledgerecommendationrecommendersemanticsystemsassistance
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information

    cs.IR 2024-12 conditional novelty 6.0 of 10

    A mixture-of-experts fusion of trainable LLM semantic embeddings with collaborative item IDs, followed by universal-then-targeted pre-training, improves sequential recommendation on Amazon and WeChat.

  2. Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph

    cs.IR 2024-11 conditional novelty 5.0 of 10

    LLM-PKG generates and prunes a product knowledge graph with LLM prompts, maps it to real inventory, and reports A/B test improvements in e-commerce recommendations.

  3. A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms

    cs.IR 2025-04 conditional novelty 4.0 of 10

    A survey that organizes foundation-model recommender systems into feature-based, generative, and agentic paradigms and reviews tasks, empirical results, and open challenges.

  4. Large Language Model Enhanced Recommender Systems: A Survey

    cs.IR 2024-12 unverdicted novelty 4.0 of 10

    A survey organizing LLM-enhanced recommender systems into knowledge, interaction, and model enhancement, and tracing a shift from explicit text to implicit embeddings and fine-tuned open-source LLMs.

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