# TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance

arXiv: https://arxiv.org/abs/2403.06642 · HTML: https://mlsanity.com/paper/2403.06642/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://mlsanity.com/paper/2305.06566/) (2023)
- [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023)
- [Large Scale Product Graph Construction for Recommendation in E-commerce](https://mlsanity.com/paper/2010.05525/) (2020)
- [Knowledge-refined Denoising Network for Robust Recommendation](https://mlsanity.com/paper/2304.14987/) (2023)

## Built on by (most cited first, 5 of 5)

- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph](https://mlsanity.com/paper/2412.01837/) (2024)
- [DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems](https://mlsanity.com/paper/2512.00596/) (2025)
- [GenOM: Ontology Matching with Description Generation and Large Language Model](https://mlsanity.com/paper/2508.10703/) (2025)
- [PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information](https://mlsanity.com/paper/2412.06308/) (2024)

## Abstract (arXiv)

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.