Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation

• URL: http://arxiv.org/abs/2608.23400v1
• Date: Mon, 24 Aug 2026 15:47:35 GMT
• Title: Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
• Abstract summary: ANR-DiffRec is a unified framework designed to encode item-based collaborative structures into discrete diffusion for generative recommendation.<n>We introduce an item-based adaptive noise rescheduling mechanism that dynamically adjusts denoising weights according to both local contextual recoverability and behavior-aware item dependencies.
• Score: 40.50371591149811
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iterative denoising. However, while effective at capturing user-level sequential patterns, these methods often fail to explicitly integrate item-based collaborative filtering information, a critical component for accurate recommendation. This deficiency manifests in two key aspects: (1) the item representation is often semantic-focused, lacking collaborative priors for diffusion training; and (2) the denoising process employs a uniform noise schedule, treating all tokens indiscriminately and ignoring item-level adaptive structural dependencies. To bridge this gap, we propose ANR-DiffRec, a unified framework designed to encode item-based collaborative structures into discrete diffusion for generative recommendation. First, we explicitly incorporate an item co-occurrence matrix to guide semantic ID generation, providing a structured collaborative prior for discrete diffusion training. Second, we introduce an item-based adaptive noise rescheduling mechanism that dynamically adjusts denoising weights according to both local contextual recoverability and behavior-aware item dependencies. Specifically, the proposed strategy jointly models intra-item structural context and inter-item collaborative signals, enabling structure-aware denoising during diffusion training. Extensive experiments on multiple benchmarks demonstrate that our method consistently outperforms state-of-the-art generative recommendation models. Code: https://github.com/CalmaQi/ANR-DiffRec.

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