mlsanityData to 14 Oct 2026

Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM

arXiv 2308.03333 ↗First posted 7 Aug 2023cs.IR cs.AIcited by 18

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Abstract (arXiv)

The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 9 of its 9 references that are in the map. First posted on arXiv 7 Aug 2023.

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9 of 9 references.

References · 9

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  6. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  7. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  8. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 402
  9. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 193

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Citing papers per year: 2023: 2; 2024: 11; 2025: 5.

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  1. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  2. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  3. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendationcited by 33
  4. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  5. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  6. 2024LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendationcited by 19
  7. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  8. 2024A Survey of Generative Search and Recommendation in the Era of Large Language Modelscited by 15
  9. 2023Adaptive In-Context Learning with Large Language Models for Bundle Generationcited by 8
  10. 2025A Survey on Large Language Models in Multimodal Recommender Systemscited by 7
  11. 2024LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Design of Multi Active/Passive Core-Agent Architecturescited by 5
  12. 2025DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systemscited by 5
6 more
  1. 2024Collaborative Cross-modal Fusion with Large Language Model for Recommendationcited by 4
  2. 2024Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectivescited by 3
  3. 2025Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographicscited by 3
  4. 2025LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clientscited by 2
  5. 2025LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosiscited by 1
  6. 2024Large Language Model Driven Recommendationcited by 0