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IPDRM: A Pyramid-Based Diffusion and Contrastive Learning Framework for Sequential Recommendation
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AI
DOI：10.1016/j.is.2025.102651.png)
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Abstract
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Sequential recommendation faces critical challenges in handling data sparsity, noise interference, and ineffective intent modeling. To address these issues, this paper proposes a novel Intent-aware Pyramid Diffusion Recommendation Model (IPDRM) that integrates hierarchical intent modeling with conditional diffusion-based augmentation. The framework employs a pyramid structure to capture multi-granular user intents (base-level item features, mid-level temporal patterns, and top-level semantic abstractions) and utilizes intent-conditioned diffusion to generate semantically consistent augmented views. Contrastive learning is then applied to align representations of original and augmented sequences. Extensive experiments on Tmall and Fliggy datasets demonstrate that IPDRM significantly outperforms state-of-the-art baselines, achieving improvements of up to 20.0% in HR@5 and 22.5% in NDCG@5. The model exhibits strong robustness in sparse and noisy scenarios, validated through comprehensive ablation studies and parameter sensitivity analyses. This work provides a effective solution for intent-aware sequential recommendation with both theoretical and practical contributions. The code for the paper is available at https://github.com/CLTCGUO/IPDRM .
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