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Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items.
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Diffusion Models Beat GANs on Image Synthesis. In NeurIPS . 8780–8794
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Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation. In WWW . ACM / IW3C2, 449–459
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Within-basket Recommendation via Neural Pattern Associator
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Cited alongside, same era.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation. In SIGIR . ACM, 238–248
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Scalable Diffusion Models with Transformers. In ICCV . IEEE, 4172–4182
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Class-Balancing Diffusion Models. In CVPR . IEEE, 18434–18443
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Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework. In The Twelfth International Conference on Learning Representations
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Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
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Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning. In CVPR . IEEE, 10844–10853
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Elucidating the Exposure Bias in Diffusion Models. In ICLR . OpenReview.net
Mang Ning, Mingxiao Li, Jianlin Su, Albert Ali Salah, and Itir Önal Ertugrul. 2024 · 2024
Later among the works it cites.
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Later among the works it cites.
Future Impact Decomposition in Request-level Recommendations. In KDD . ACM, 5905–5916
Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, and Guangming Xie. 2024 · 2024
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Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming. In The Thirteenth International Conference on Learning Representations
Haoyang Liu, Jie Wang, Zijie Geng, Xijun Li, Yuxuan Zong, Fangzhou Zhu, Jianye HAO, and Feng Wu. 2025 · 2025
Closest in time.
Sequential Recommendation via Stochastic Self-Attention. In WWW . ACM, 2036–2047
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S. Yu. 2022 · 2047
Closest in time.
Coarse-to-Fine Sparse Sequential Recommendation. In SIGIR . ACM, 2082–2086
Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel, and Julian J. McAuley. 2022 · 2086
Closest in time.