Graph-to-Sequence Knowledge Distillation Method for Sequential Recommendation

The graph structure of user-item interactions and users' sequential behaviors are two fundamental signals in sequential recommendation (SR). Graph-based models excel at capturing high-order connectivity in the interaction graph, while sequential models are effective in modeling dynamic user preferences. However, the two embedding models learn different representation spaces, and naive fusion strategies often suffer from representation conflicts. To bridge this gap, we design FROm Graph to Sequence Knowledge Distillation (FROGS), a theoretically grounded method that transfers structural knowledge from a graph-based teacher to a sequential student model. Under common assumptions on embeddings, noise, and sample size, we derive upper bounds on the prediction error of three schemes, sequential-only, direct fusion, and graph-to-sequence distillation. Theoretically, we establish a distillability criterion for when knowledge distillation improves the prediction. We also analyze the bias-variance trade-off in FROGS distillation. Empirically, experiments on publicly available datasets and real-world data from an aggregated payment platform demonstrate consistent improvements over the evaluated baselines.