mlsanityData to 7 Oct 2026

GPT4Rec: Graph Prompt Tuning for Streaming Recommendation

arXiv 2406.08229 ↗First posted 12 Jun 2024cs.IR cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional models, typically reliant on a static training-test approach, struggle to keep pace with these dynamic demands. Streaming recommendation, particularly through continual graph learning, has emerged as a novel solution. However, existing methods in this area either rely on historical data replay, which is increasingly impractical due to stringent data privacy regulations; or are inability to effectively address the over-stability issue; or depend on model-isolation and expansion strategies. To tackle these difficulties, we present GPT4Rec, a Graph Prompt Tuning method for streaming Recommendation. Given the evolving user-item interaction graph, GPT4Rec first disentangles the graph patterns into multiple views. After isolating specific interaction patterns and relationships in different views, GPT4Rec utilizes lightweight graph prompts to efficiently guide the model across varying interaction patterns within the user-item graph. Firstly, node-level prompts are employed to instruct the model to adapt to changes in the attributes or properties of individual nodes within the graph. Secondly, structure-level prompts guide the model in adapting to broader patterns of connectivity and relationships within the graph. Finally, view-level prompts are innovatively designed to facilitate the aggregation of information from multiple disentangled views. These prompt designs allow GPT4Rec to synthesize a comprehensive understanding of the graph, ensuring that all vital aspects of the user-item interactions are considered and effectively integrated. Experiments on four diverse real-world datasets demonstrate the effectiveness and efficiency of our proposal.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

39 of 72 references.

References · 39

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  6. 2021Learning to Prompt for Continual Learningcited by 938
  7. 2022CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learningcited by 446
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  10. 2019On Tiny Episodic Memories in Continual Learningcited by 299
  11. 2018Learning without Memorizingcited by 249
  12. 2018Selective Experience Replay for Lifelong Learningcited by 239
27 more
  1. 2023Progressive Prompts: Continual Learning for Language Modelscited by 195
  2. 2023Is ChatGPT a Good Recommender? A Preliminary Studycited by 192
  3. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  4. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 153
  5. 2019Continual Learning via Neural Pruningcited by 101
  6. 2014Online Clustering of Banditscited by 99
  7. 2020Overcoming Catastrophic Forgetting in Graph Neural Networkscited by 93
  8. 2018Streaming Graph Neural Networkscited by 91
  9. 2020Streaming Graph Neural Networks via Continual Learningcited by 63
  10. 2021Continual Learning via Local Module Compositioncited by 61
  11. 2023HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learningcited by 45
  12. 2016On Context-Dependent Clustering of Banditscited by 43
  13. 2020GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systemscited by 42
  14. 2019Improved Algorithm on Online Clustering of Banditscited by 38
  15. 2022Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Networkcited by 38
  16. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  17. 2023Dynamically Expandable Graph Convolution for Streaming Recommendationcited by 26
  18. 2020GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendationcited by 24
  19. 2020ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendationcited by 22
  20. 2016Streaming Recommender Systemscited by 17
  21. 2022Code Recommendation for Open Source Software Developerscited by 13
  22. 2024Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspectivecited by 13
  23. 2015Dynamic Matrix Factorization with Priors on Unknown Valuescited by 11
  24. 2022Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendationcited by 11
  25. 2020Memory Augmented Neural Model for Incremental Session-based Recommendationcited by 9
  26. 2023A Survey on Incremental Update for Neural Recommender Systemscited by 8
  27. 2023POP: Prompt Of Prompts for Continual Learningcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuningcited by 13
  2. 2025Graph Prompting for Graph Learning Models: Recent Advances and Future Directionscited by 3
  3. 2025Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generationcited by 2
  4. 2024Towards Graph Prompt Learning: A Survey and Beyondcited by 1
  5. 2025Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuningcited by 0
  6. 2026GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networkscited by 0
  7. 2026EviRec: Continual Evidence Learning for Dual Cold-Start POI Recommendationcited by 0