Interaction-Specific Diffusion Scheduling Guided by Temporal Priors for Sequential Recommendation

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Sequential recommendation must infer evolving preferences from interaction histories that are sparse, temporally irregular, and heterogeneous in relevance. Existing diffusion-based recommenders improve representation generation and recovery, yet their perturbation strength is typically governed by the diffusion step and does not explicitly reflect the temporal state of each historical interaction. We propose ISDSRec, an interaction-specific diffusion scheduling framework that uses local interaction gaps to represent behavioral rhythm and global recency to derive a position-specific temporal prior. This prior modulates a shared base diffusion schedule so that interactions at the same diffusion step can receive different effective noise intensities. A diffusion-step- and recency-conditioned bidirectional Transformer denoiser predicts the injected Gaussian noise, from which clean interaction representations are recovered analytically. The same temporal prior is reused as a recency bias in long-term attention, while behavioral statistics regulate long- and short-term preference fusion. Item textual metadata are encoded offline by a frozen GTE-Qwen2-7B-instruct model and adaptively fused with trainable ID embeddings. Experiments on three Amazon Review categories and MovieLens-1M show that ISDSRec achieves the highest mean performance across all 16 dataset–metric settings at K = 5 and K = 10, with relative gains of 6.17–8.55% over the strongest competing baselines. Controlled analyses examine the contributions of semantic representation, diffusion-based recovery, interaction-specific scheduling, and preference reconstruction. NDCG@10 improvements over shuffled-recency, reversed-recency, position-based scheduling, Clean-Input Transformer, and Temporal-GTE SASRec controls reach Holm-adjusted significance across all four datasets. NDCG@10 improvements over Noise-Matched Uniform and the Heteroscedastic Gaussian DAE reach Holm-adjusted significance only on MovieLens-1M. Stratified, stochastic-inference, and efficiency analyses further characterize the model under different temporal, behavioral, and practical inference conditions.