{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:15:22Z","timestamp":1785543322866,"version":"3.56.0"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            Mainstream solutions to sequential recommendation represent items with fixed vectors. These vectors have limited capability in capturing items\u2019 latent aspects and users\u2019 diverse preferences. As a new generative paradigm,\n            <jats:italic>diffusion models<\/jats:italic>\n            have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this article, we make the very first attempt to adapt the diffusion model to sequential recommendation and propose\n            <jats:sc>DiffuRec<\/jats:sc>\n            for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in\n            <jats:sc>DiffuRec<\/jats:sc>\n            , which reflect a user\u2019s multiple interests and an item\u2019s various aspects adaptively. In the diffusion phase,\n            <jats:sc>DiffuRec<\/jats:sc>\n            corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterward, the item representation is fed into an approximator for target item representation reconstruction. In the reverse phase, based on a user\u2019s historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that\n            <jats:sc>DiffuRec<\/jats:sc>\n            outperforms strong baselines by a large margin.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3631116","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T11:18:45Z","timestamp":1703848725000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":125,"title":["DiffuRec: A Diffusion Model for Sequential Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7840-3364","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministryof Education, School of Cyber Science and Engineering, Wuhan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Aixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0764-4258","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1986.10488093"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Ruojin Cai Guandao Yang Hadar Averbuch-Elor Zekun Hao Serge Belongie Noah Snavely and Bharath Hariharan. 2020. Learning gradient fields for shape generation. In Computer Vision\u2014ECCV 2020 . Lecture Notes in Computer Science Vol. 12348. Springer 364\u2013381.","DOI":"10.1007\/978-3-030-58580-8_22"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403344"},{"key":"e_1_3_2_6_2","article-title":"WaveGrad: Estimating gradients for waveform generation","author":"Chen Nanxin","year":"2020","unstructured":"Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, and William Chan. 2020. WaveGrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713 (2020).","journal-title":"arXiv preprint arXiv:2009.00713"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159668"},{"key":"e_1_3_2_8_2","article-title":"Continuous diffusion for categorical data","author":"Dieleman Sander","year":"2022","unstructured":"Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et\u00a0al. 2022. Continuous diffusion for categorical data. arXiv preprint arXiv:2211.15089 (2022).","journal-title":"arXiv preprint arXiv:2211.15089"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460426.3463638"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482145"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512077"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"e_1_3_2_13_2","article-title":"DiffuSeq: Sequence to sequence text generation with diffusion models","author":"Gong Shansan","year":"2022","unstructured":"Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. 2022. DiffuSeq: Sequence to sequence text generation with diffusion models. arXiv preprint arXiv:2210.08933 (2022).","journal-title":"arXiv preprint arXiv:2210.08933"},{"key":"e_1_3_2_14_2","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).","journal-title":"arXiv preprint arXiv:1412.6572"},{"key":"e_1_3_2_15_2","article-title":"DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation","author":"Guo Lei","year":"2021","unstructured":"Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv preprint arXiv:2105.03300 (2021).","journal-title":"arXiv preprint arXiv:2105.03300"},{"key":"e_1_3_2_16_2","article-title":"SSD-LM: Semi-autoregressive simplex-based diffusion language model for text generation and modular control","author":"Han Xiaochuang","year":"2022","unstructured":"Xiaochuang Han, Sachin Kumar, and Yulia Tsvetkov. 2022. SSD-LM: Semi-autoregressive simplex-based diffusion language model for text generation and modular control. arXiv preprint arXiv:2210.17432 (2022).","journal-title":"arXiv preprint arXiv:2210.17432"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209981"},{"key":"e_1_3_2_18_2","article-title":"DiffusionBERT: Improving generative masked language models with diffusion models","author":"He Zhengfu","year":"2022","unstructured":"Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, and Xipeng Qiu. 2022. DiffusionBERT: Improving generative masked language models with diffusion models. arXiv preprint arXiv:2211.15029 (2022).","journal-title":"arXiv preprint arXiv:2211.15029"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_20_2","article-title":"Session-based recommendations with recurrent neural networks","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).","journal-title":"arXiv preprint arXiv:1511.06939"},{"key":"e_1_3_2_21_2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840\u20136851.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_22_2","first-page":"Article 47, 33","article-title":"Cascaded diffusion models for high fidelity image generation.","volume":"23","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. 2022. Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research 23 (2022), Article 47, 33 pages.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_23_2","article-title":"Video diffusion models","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet. 2022. Video diffusion models. arXiv preprint arXiv:2204.03458 (2022).","journal-title":"arXiv preprint arXiv:2204.03458"},{"key":"e_1_3_2_24_2","article-title":"Argmax flows and multinomial diffusion: Towards non-autoregressive language models","author":"Hoogeboom Emiel","year":"2021","unstructured":"Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forr\u00e9, and Max Welling. 2021. Argmax flows and multinomial diffusion: Towards non-autoregressive language models. arXiv preprint arXiv:2102.05379 (2021).","journal-title":"arXiv preprint arXiv:2102.05379"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/1944339.1944341"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535335"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357814"},{"key":"e_1_3_2_29_2","article-title":"Diffusion-LM improves controllable text generation","author":"Li Xiang Lisa","year":"2022","unstructured":"Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto. 2022. Diffusion-LM improves controllable text generation. arXiv preprint arXiv:2205.14217 (2022).","journal-title":"arXiv preprint arXiv:2205.14217"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_31_2","article-title":"TESS: Text-to-text self-conditioned simplex diffusion","author":"Mahabadi Rabeeh Karimi","year":"2023","unstructured":"Rabeeh Karimi Mahabadi, Jaesung Tae, Hamish Ivison, James Henderson, Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2023. TESS: Text-to-text self-conditioned simplex diffusion. arXiv preprint arXiv:2305.08379 (2023).","journal-title":"arXiv preprint arXiv:2305.08379"},{"key":"e_1_3_2_32_2","first-page":"8162","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Nichol Alexander Quinn","year":"2021","unstructured":"Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In Proceedings of the International Conference on Machine Learning. 8162\u20138171."},{"key":"e_1_3_2_33_2","article-title":"Hierarchical text-conditional image generation with clip latents","author":"Ramesh Aditya","year":"2022","unstructured":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022).","journal-title":"arXiv preprint arXiv:2204.06125"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Olaf Ronneberger Philipp Fischer and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015 . Lecture Notes in Computer Science Vol. 9351. Springer 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291007"},{"key":"e_1_3_2_38_2","article-title":"Image super-resolution via iterative refinement","author":"Saharia Chitwan","year":"2023","unstructured":"Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. 2023. Image super-resolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2023), 4713\u20134726.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"9","key":"e_1_3_2_39_2","article-title":"An MDP-based recommender system.","volume":"6","author":"Shani Guy","year":"2005","unstructured":"Guy Shani, David Heckerman, Ronen I. Brafman, and Craig Boutilier. 2005. An MDP-based recommender system. Journal of Machine Learning Research 6, 9 (2005), 1265\u20131295.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_40_2","article-title":"Sequential recommendation with user evolving preference decomposition","author":"Shao Weiqi","year":"2022","unstructured":"Weiqi Shao, Xu Chen, Long Xia, Jiashu Zhao, and Dawei Yin. 2022. Sequential recommendation with user evolving preference decomposition. arXiv preprint arXiv:2203.16942 (2022).","journal-title":"arXiv preprint arXiv:2203.16942"},{"key":"e_1_3_2_41_2","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. 2256\u20132265."},{"key":"e_1_3_2_42_2","article-title":"Self-conditioned embedding diffusion for text generation","author":"Strudel Robin","year":"2022","unstructured":"Robin Strudel, Corentin Tallec, Florent Altch\u00e9, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, et\u00a0al. 2022. Self-conditioned embedding diffusion for text generation. arXiv preprint arXiv:2211.04236 (2022).","journal-title":"arXiv preprint arXiv:2211.04236"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16564"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532081"},{"issue":"11","key":"e_1_3_2_47_2","article-title":"Visualizing data using t-SNE.","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_48_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557464"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401134"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412258"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313603"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449873"},{"key":"e_1_3_2_55_2","article-title":"Diffusion models: A comprehensive survey of methods and applications","author":"Yang Ling","year":"2022","unstructured":"Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796 (2022).","journal-title":"arXiv preprint arXiv:2209.00796"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380116"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512094"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450086"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124300"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015885"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512111"},{"key":"e_1_3_2_63_2","article-title":"Using temporal data for making recommendations","author":"Zimdars Andrew","year":"2013","unstructured":"Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. 2013. Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320 (2013).","journal-title":"arXiv preprint arXiv:1301.2320"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3631116","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3631116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:51Z","timestamp":1750178151000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3631116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3631116"],"URL":"https:\/\/doi.org\/10.1145\/3631116","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]},"assertion":[{"value":"2023-04-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}