GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang, Yuchen Yan, Xi Zhang, Liying Kang, Chaozhuo Li, Feiran, Huang, Senzhang Wang, Sunghun Kim

TL;DR
GPT4Rec introduces a graph prompt tuning approach for streaming recommendation systems, enabling dynamic adaptation to evolving user-item interactions without relying on historical data replay or model expansion.
Contribution
It proposes a novel graph prompt tuning method that disentangles interaction patterns into multiple views and uses lightweight prompts for efficient, adaptive streaming recommendations.
Findings
Outperforms existing methods on four real-world datasets
Effectively adapts to evolving user preferences and graph changes
Reduces reliance on historical data replay and model expansion
Abstract
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.…
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