ScoreRec: Reallocating Probability Mass in Sequential Recommendation via Score-based Diffusion Modeling

Abstract

Sequential recommendation generates personalized item rankings by estimating latent user-preference distributions from historical interactions. Although Diffusion Models (DMs) have gained prominence due to their exceptional capacity to capture complex distributions, current DM-based recommenders exhibit a critical limitation Distributional Misalignment, where mode-seeking denoising processes disproportionately allocate probability mass to popular items, thereby diverging from users’ genuine preferences. To bridge this gap, we introduce ScoreRec, a novel sequential recommendation framework that reformulates the recommendation task as a continuous-time score-based generative process. Unlike conventional discrete denoising diffusion probabilistic models (DDPMs) that rely on fixed time-step approximations, ScoreRec models the diffusion process via a Stochastic Differential Equation. This formulation allows ScoreRec to estimate the gradient of the log-density with infinite temporal resolution, effectively reallocating probability mass to low-frequency yet personally relevant items and thereby mitigating the mode-seeking bias. Specifically, ScoreRec leverages a stochastic differential equation with a continuous-time noise schedule, ensuring that the noise level is dynamically adjusted to the local embedding density. During inference, an adaptive-step-size solver integrates the corresponding probability-flow ordinary differential equation to deterministically generate recommendations, striking a principled balance between accuracy and efficiency. Comprehensive experiments on three public benchmarks demonstrate that ScoreRec consistently surpasses eleven state-of-the-art baselines in both accuracy and diversity. Our code is publicly available at: https://github.com/AONE-NLP/ScoreRec.