Graph Neural Networks for Incremental Service Recommendation with Dynamic Interest Alignment

In the realm of service recommendation, personalized systems are essential for addressing information overload and satisfying diverse user preferences. However, traditional models, bound by a static training-test framework, often fail to keep pace with the dynamic nature of evolving user interests and expanding service catalogs. To tackle these challenges, we introduce an innovative incremental recommendation strategy that employs graph neural network(GNN) fine-tuning and interest alignment. This method dynamically updates users' latest interests while aligning them with pertinent long-term interests, effectively preventing catastrophic forgetting and minimizing dependency on historical data. Our approach not only ensures responsiveness to the most recent user interactions but also preserves valuable prior interests, demonstrating exceptional adaptability and performance in dynamic service recommendation environments. Furthermore, our strategy is designed to be seamlessly integrated into various existing GNN-based recommendation models. Extensive experiments conducted on three industrial datasets demonstrate the effectiveness and robustness of our method, highlighting its practical applicability and superior performance in dynamic recommendation scenarios.