mlsanityData to 7 Oct 2026

LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

arXiv 2402.09617 ↗First posted 14 Feb 2024cs.AI cs.IRcited by 24

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Abstract (arXiv)

Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models, representing a textual generative perspective, excel at modeling user languages, understanding behavioral contexts, capturing user-item semantic relationships, analyzing textual sentiments, and generating coherent and contextually relevant texts as recommendations. However, there is a gap between the connected graph perspective and the text generation perspective as the task formulations are different. A research question arises: how can we effectively integrate the two perspectives for more personalized recsys? To fill this gap, we propose to incorporate graph-edge information into LLMs via prompt and attention innovations. We reformulate recommendations as a probabilistic generative problem using prompts. We develop a framework to incorporate graph edge information from the prompt and attention mechanisms for graph-structured LLM recommendations. We develop a new prompt design that brings in both first-order and second-order graph relationships; we devise an improved LLM attention mechanism to embed direct the spatial and connectivity information of edges. Our evaluation of real-world datasets demonstrates the framework's ability to understand connectivity information in graph data and to improve the relevance and quality of recommendation results.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 48 of its 82 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

48 of 82 references.

References · 48

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
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  11. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
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36 more
  1. 2019Neural Graph Collaborative Filteringcited by 660
  2. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  3. 2023A Survey on Large Language Models for Recommendationcited by 378
  4. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  5. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  6. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  7. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  8. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  9. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 307
  10. 2018xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemscited by 277
  11. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 241
  12. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  13. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  14. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 160
  15. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  16. 2015VBPR: Visual Bayesian Personalized Ranking from Implicit Feedbackcited by 151
  17. 2023RecMind: Large Language Model Powered Agent For Recommendationcited by 129
  18. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 120
  19. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119
  20. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  21. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 99
  22. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  23. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 10; 2026: 5.

24 papers.

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  1. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  2. 2025MixLLM: Dynamic Routing in Mixed Large Language Modelscited by 56
  3. 2025SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluationcited by 32
  4. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  5. 2025Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teamingcited by 11
  6. 2024Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenizationcited by 9
  7. 2024Enhancing High-order Interaction Awareness in LLM-based Recommender Modelcited by 8
  8. 2024LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modelingcited by 7
  9. 2025Efficient Post-Training Refinement of Latent Reasoning in Large Language Modelscited by 7
  10. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
  11. 2025LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformationcited by 5
  12. 2024Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendationcited by 4
12 more
  1. 2025Bridging the Domain Gap in Equation Distillation with Reinforcement Feedbackcited by 4
  2. 2024CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomicscited by 3
  3. 2025Graph Foundation Models for Recommendation: A Comprehensive Surveycited by 3
  4. 2026Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluationcited by 3
  5. 2024Topic-Aware Knowledge Graph with Large Language Models for Interoperability in Recommender Systemscited by 2
  6. 2025PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Modelscited by 2
  7. 2024Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Modelscited by 1
  8. 2026Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendationcited by 1
  9. 2026Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendationcited by 1
  10. 2025When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMscited by 0
  11. 2026Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendationcited by 0
  12. 2026CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Servicescited by 0