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Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions.
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Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation. In Proceedings of the 15th ACM conference on recommender systems . 143–153
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Session-based Recommendations with Recurrent Neural Networks. In ICLR (Poster)
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LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (<conf-loc>, <city>Taipei</city>, <country>Taiwan</country>, </conf-loc>) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 289–299
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Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, and James Caverlee. 2024a · 2024
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Vmamba: Visual state space model
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Jun Ma, Feifei Li, and Bo Wang. 2024 · 2024
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Rethinking Large Language Model Architectures for Sequential Recommendations
Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, Devendra Yadav, Fei Wang, Zhen Wen, Jiliang Tang, and Hui Liu. 2024 · 2024
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Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. 2024 · 2024
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