  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.
