# GPT4Rec: Graph Prompt Tuning for Streaming Recommendation arXiv: https://arxiv.org/abs/2406.08229 ยท HTML: https://mlsanity.com/paper/2406.08229/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 39 of its 72 references that are in the map. - First posted on arXiv 12 Jun 2024. ## Builds on (most cited first, 30 of 39) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021) - [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016) - [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021) - [CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2211.13218/) (2022) - [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019) - [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018) - [On Tiny Episodic Memories in Continual Learning](https://mlsanity.com/paper/1902.10486/) (2019) - [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018) - [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018) - [Progressive Prompts: Continual Learning for Language Models](https://mlsanity.com/paper/2301.12314/) (2023) - [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023) - [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://mlsanity.com/paper/2302.08043/) (2023) - [Learning on Large-scale Text-attributed Graphs via Variational Inference](https://mlsanity.com/paper/2210.14709/) (2022) - [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014) - [Continual Learning via Neural Pruning](https://mlsanity.com/paper/1903.04476/) (2019) - [Overcoming Catastrophic Forgetting in Graph Neural Networks](https://mlsanity.com/paper/2012.06002/) (2020) - [Streaming Graph Neural Networks](https://mlsanity.com/paper/1810.10627/) (2018) - [Streaming Graph Neural Networks via Continual Learning](https://mlsanity.com/paper/2009.10951/) (2020) - [Continual Learning via Local Module Composition](https://mlsanity.com/paper/2111.07736/) (2021) - [HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning](https://mlsanity.com/paper/2312.01878/) (2023) - [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016) - [GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems](https://mlsanity.com/paper/2008.13517/) (2020) - [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019) - [Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network](https://mlsanity.com/paper/2206.12781/) (2022) - [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021) - [Dynamically Expandable Graph Convolution for Streaming Recommendation](https://mlsanity.com/paper/2303.11700/) (2023) - [GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation](https://mlsanity.com/paper/2007.02747/) (2020) ## Built on by (most cited first, 7 of 7) - [GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning](https://mlsanity.com/paper/2511.05592/) (2025) - [Graph Prompting for Graph Learning Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2506.08326/) (2025) - [Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation](https://mlsanity.com/paper/2510.11541/) (2025) - [Towards Graph Prompt Learning: A Survey and Beyond](https://mlsanity.com/paper/2408.14520/) (2024) - [Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning](https://mlsanity.com/paper/2507.03047/) (2025) - [GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks](https://mlsanity.com/paper/2602.11629/) (2026) - [EviRec: Continual Evidence Learning for Dual Cold-Start POI Recommendation](https://mlsanity.com/paper/2609.20313/) (2026) ## 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.