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RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations

arXiv 2509.06286 ↗First posted 8 Sep 2025cs.LGcited by 0

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Abstract (arXiv)

Personalization is a core capability across consumer technologies, streaming, shopping, wearables, and voice, yet it remains challenged by sparse interactions, fast content churn, and heterogeneous textual signals. We present RecMind, an LLM-enhanced graph recommender that treats the language model as a preference prior rather than a monolithic ranker. A frozen LLM equipped with lightweight adapters produces text-conditioned user/item embeddings from titles, attributes, and reviews; a LightGCN backbone learns collaborative embeddings from the user-item graph. We align the two views with a symmetric contrastive objective and fuse them via intra-layer gating, allowing language to dominate in cold/long-tail regimes and graph structure to stabilize rankings elsewhere. On Yelp and Amazon-Electronics, RecMind attains the best results on all eight reported metrics, with relative improvements up to +4.53% (Recall@40) and +4.01% (NDCG@40) over strong baselines. Ablations confirm both the necessity of cross-view alignment and the advantage of gating over late fusion and LLM-only variants.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 8 Sep 2025.

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References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2018Self-Attentive Sequential Recommendationcited by 1,184
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  7. 2019Neural Graph Collaborative Filteringcited by 661
  8. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  9. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  10. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  11. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  12. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 172
13 more
  1. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  2. 2023GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretationcited by 103
  3. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 100
  4. 2017Joint Deep Modeling of Users and Items Using Reviews for Recommendationcited by 84
  5. 2019Neural News Recommendation with Attentive Multi-View Learningcited by 59
  6. 2022XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendationcited by 57
  7. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  8. 2024Large Language Models Enhanced Collaborative Filteringcited by 27
  9. 2024Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Samplingcited by 5
  10. 2025Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretrainingcited by 5
  11. 2024Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learningcited by 2
  12. 2024FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNscited by 1
  13. 2025ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detectioncited by 1

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