mlsanityData to 7 Oct 2026

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

arXiv 2307.05722 ↗First posted 10 Jul 2023cs.AI cs.CL cs.IRcited by 45

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Abstract (arXiv)

Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for behavior graph understanding in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application. We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs and uncover underlying patterns and relationships. Specifically, we propose a meta-path prompt constructor that leverages LLM recommender to understand behavior graphs for the first time and design a corresponding path augmentation module to alleviate the prompt bias introduced by path-based sequence input. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users. We evaluate the effectiveness of our approach on a comprehensive dataset and demonstrate its ability to improve the relevance and quality of recommended quality. This research not only sheds light on the untapped potential of large language models but also provides valuable insights for developing advanced recommendation systems in the recruitment market. The findings contribute to the growing field of natural language processing and offer practical implications for enhancing job search experiences. We release the code at https://github.com/WLiK/GLRec.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

19 of 33 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2020Heterogeneous Graph Transformercited by 581
  5. 2023A Survey on Large Language Models for Recommendationcited by 378
  6. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  7. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  8. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  9. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
  10. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  11. 2018Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approachcited by 37
  12. 2020What does BERT know about books, movies and music? Probing BERT for Conversational Recommendationcited by 36
7 more
  1. 2023Prompt Learning for News Recommendationcited by 25
  2. 2020Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Networkcited by 23
  3. 2018Towards Deep and Representation Learning for Talent Search at LinkedIncited by 18
  4. 2022Modeling Two-Way Selection Preference for Person-Job Fitcited by 18
  5. 2022Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defensecited by 16
  6. 2023Generative Job Recommendations with Large Language Modelcited by 13
  7. 2021UserBERT: Contrastive User Model Pre-trainingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 23; 2025: 12; 2026: 5.

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  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  2. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systemscited by 116
  3. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 97
  4. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  5. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  6. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  7. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  8. 2023DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendationcited by 27
  9. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  10. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  11. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  12. 2024Make Large Language Model a Better Rankercited by 11
33 more
  1. 2024DynLLM: When Large Language Models Meet Dynamic Graph Recommendationcited by 11
  2. 2024Large Language Model with Graph Convolution for Recommendationcited by 10
  3. 2024Large Language Model Enhanced Recommender Systems: A Surveycited by 10
  4. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  5. 2024Natural Language Processing for Human Resources: A Surveycited by 9
  6. 2025Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AIcited by 9
  7. 2024LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modelingcited by 7
  8. 2024Multi-View Empowered Structural Graph Wordification for Language Modelscited by 7
  9. 2024LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generationcited by 6
  10. 2024Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learningcited by 5
  11. 2024A Survey on Large Language Models from Concept to Implementationcited by 4
  12. 2024UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Modelscited by 4
  13. 2025Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMscited by 4
  14. 2024Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarkingcited by 3
  15. 2024Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systemscited by 3
  16. 2025Graph Foundation Models for Recommendation: A Comprehensive Surveycited by 3
  17. 2025Unify Graph Learning with Text: Unleashing LLM Potentials for Session Searchcited by 3
  18. 2024Leveraging Large Language Models for Hybrid Workplace Decision Supportcited by 2
  19. 2024Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learningcited by 1
  20. 2024Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Informationcited by 1
  21. 2025LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Rerankingcited by 1
  22. 2025AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Modelscited by 1
  23. 2025Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?cited by 1
  24. 2025Enhance Large Language Models as Recommendation Systems with Collaborative Filteringcited by 1
  25. 2025Iterative Critique-Refine Framework for Enhancing LLM Personalizationcited by 1
  26. 2024DrLLM: Prompt-Enhanced Distributed Denial-of-Service Resistance Method with Large Language Modelscited by 0
  27. 2025MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Modelcited by 0
  28. 2025LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanationcited by 0
  29. 2026Ego: Embedding-Guided Personalization of Vision-Language Modelscited by 0
  30. 2026Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendationcited by 0
  31. 2026On Reasoning Behind Next Occupation Recommendationcited by 0
  32. 2026An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendationcited by 0
  33. 2026SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attackcited by 0