{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:24:45Z","timestamp":1791566685325,"version":"4.3.4"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation","award":["2021-0-00347"],"award-info":[{"award-number":["2021-0-00347"]}]},{"DOI":"10.13039\/501100006374","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C3004345, RS-2023-00220762"],"award-info":[{"award-number":["2021R1A2C3004345, RS-2023-00220762"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,10]]},"DOI":"10.1145\/3626772.3657742","type":"proceedings-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T12:40:05Z","timestamp":1720701605000},"page":"1360-1369","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3414-630X","authenticated-orcid":false,"given":"Yu","family":"Hou","sequence":"first","affiliation":[{"name":"Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7906-8475","authenticated-orcid":false,"given":"Jin-Duk","family":"Park","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6533-3469","authenticated-orcid":false,"given":"Won-Yong","family":"Shin","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Martin Arjovsky and L\u00e9on Bottou. 2017. Towards principled methods for training generative adversarial networks. In ICLR."},{"key":"e_1_3_2_1_2_1","unstructured":"Jacob Austin Daniel D Johnson Jonathan Ho Daniel Tarlow and Rianne Van Den Berg. 2021. Structured denoising diffusion models in discrete state-spaces. In NeurIPS. 17981--17993."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Omri Avrahami Dani Lischinski and Ohad Fried. 2022. Blended diffusion for text-driven editing of natural images. In CVPR. 18208--18218.","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"e_1_3_2_1_4_1","unstructured":"Rianne van den Berg Thomas N Kipf and Max Welling. 2018. Graph convolutional matrix completion. In KDD (Deep Learning Day)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Homanga Bharadhwaj Homin Park and Brian Y Lim. 2018. RecGAN: Recurrent generative adversarial networks for recommendation systems. In RecSys. 372--376.","DOI":"10.1145\/3240323.3240383"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271743"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373807"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Jeongwhan Choi Seoyoung Hong Noseong Park and Sung-Bae Cho. 2023. Blurring-sharpening process models for collaborative filtering. In SIGIR. 1096--1106.","DOI":"10.1145\/3539618.3591645"},{"key":"e_1_3_2_1_9_1","unstructured":"Kamil Deja Anna Kuzina Tomasz Trzcinski and Jakub Tomczak. 2022. On analyzing generative and denoising capabilities of diffusion-based deep generative models. In NeurIPS. 26218--26229."},{"key":"e_1_3_2_1_10_1","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. In NeurIPS. 8780--8794."},{"key":"e_1_3_2_1_11_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In NeurIPS. 2672--2680."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3028572"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Kuan Deng Xiang Wang Yan Li Yongdong Zhang and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_15_1","unstructured":"Jonathan Ho Ajay Jain and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In NeurIPS. 6840--6851."},{"key":"e_1_3_2_1_16_1","volume-title":"Jae Hyun Lim, and Aaron C Courville","author":"Huang Chin-Wei","year":"2021","unstructured":"Chin-Wei Huang, Jae Hyun Lim, and Aaron C Courville. 2021. A variational perspective on diffusion-based generative models and score matching. In NeurIPS. 22863--22876."},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A method for stochastic optimization. In ICLR (Poster).","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR (Poster)."},{"key":"e_1_3_2_1_18_1","unstructured":"Diederik P Kingma and Max Welling. 2014. Auto-encoding variational bayes. In ICLR."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In KDD. 305--314.","DOI":"10.1145\/3097983.3098077"},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"DiffuRec: A diffusion model for sequential recommendation","volume":"42","author":"Li Zihao","year":"2023","unstructured":"Zihao Li, Aixin Sun, and Chenliang Li. 2023. DiffuRec: A diffusion model for sequential recommendation. ACM Transactions on Information Systems , Vol. 42, 3 (2023), 1--28.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Dawen Liang Rahul G Krishnan Matthew D Hoffman and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Zihan Lin Changxin Tian Yupeng Hou and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW. 2320--2329.","DOI":"10.1145\/3485447.3512104"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Qidong Liu Fan Yan Xiangyu Zhao Zhaocheng Du Huifeng Guo Ruiming Tang and Feng Tian. 2023. Diffusion augmentation for sequential recommendation. In CIKM. 1576--1586.","DOI":"10.1145\/3583780.3615134"},{"key":"e_1_3_2_1_25_1","unstructured":"Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. In NeurIPS. 5712--5723."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Kelong Mao Jieming Zhu Xi Xiao Biao Lu Zhaowei Wang and Xiuqiang He. 2021. UltraGCN: Ultra simplification of graph convolutional networks for recommendation. In CIKM. 1253--1262.","DOI":"10.1145\/3459637.3482291"},{"key":"e_1_3_2_1_27_1","unstructured":"Luke Metz Ben Poole David Pfau and Jascha Sohl-Dickstein. 2017. Unrolled generative adversarial networks. In ICLR (Poster)."},{"key":"e_1_3_2_1_28_1","unstructured":"Andriy Mnih and Russ R Salakhutdinov. 2007. Probabilistic matrix factorization. In NeurIPS. 1257--1264."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Jin-Duk Park Siqing Li Xin Cao and Won-Yong Shin. 2023. Criteria tell you more than ratings: Criteria preference-aware light graph convolution for effective multi-criteria recommendation. In KDD. 1808--1819.","DOI":"10.1145\/3580305.3599292"},{"key":"e_1_3_2_1_30_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Robin Rombach Andreas Blattmann Dominik Lorenz Patrick Esser and Bj\u00f6rn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In CVPR. 10684--10695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_1_32_1","unstructured":"Chitwan Saharia William Chan and et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. In NeurIPS. 36479--36494."},{"key":"e_1_3_2_1_33_1","unstructured":"Nikolay Savinov Junyoung Chung Mikolaj Binkowski Erich Elsen and Aaron van den Oord. 2022. Step-unrolled denoising autoencoders for text generation. In ICLR."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Yifei Shen Yongji Wu Yao Zhang Caihua Shan Jun Zhang B Khaled Letaief and Dongsheng Li. 2021. How powerful is graph convolution for recommendation?. In CIKM. 1619--1629.","DOI":"10.1145\/3459637.3482264"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Ilya Shenbin Anton Alekseev Elena Tutubalina Valentin Malykh and Sergey I Nikolenko. 2020. RecVAE: A new variational autoencoder for top-N recommendations with implicit feedback. In WSDM. 528--536.","DOI":"10.1145\/3336191.3371831"},{"key":"e_1_3_2_1_36_1","unstructured":"Jascha Sohl-Dickstein Eric Weiss Niru Maheswaranathan and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML. 2256--2265."},{"key":"e_1_3_2_1_37_1","unstructured":"Yang Song Jascha Sohl-Dickstein Diederik P Kingma Abhishek Kumar Stefano Ermon and Ben Poole. 2020. Score-based generative modeling through stochastic differential equations. In ICLR."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Jianing Sun Yingxue Zhang Wei Guo Huifeng Guo Ruiming Tang Xiuqiang He Chen Ma and Mark Coates. 2020. Neighbor interaction aware graph convolution networks for recommendation. In SIGIR. 1289--1298.","DOI":"10.1145\/3397271.3401123"},{"key":"e_1_3_2_1_39_1","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. In NeurIPS. 6000--6010."},{"key":"e_1_3_2_1_40_1","volume-title":"Recommendation via collaborative diffusion generative model","author":"Walker Joojo","unstructured":"Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao, and Fan Zhou. 2022. Recommendation via collaborative diffusion generative model. In KSEM. Springer, 593--605."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_1_42_1","volume-title":"Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768","author":"Wang Sinong","year":"2020","unstructured":"Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma. 2020. Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Wenjie Wang Yiyan Xu Fuli Feng Xinyu Lin Xiangnan He and Tat-Seng Chua. 2023. Diffusion recommender model. In SIGIR. 832--841.","DOI":"10.1145\/3539618.3591663"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Qiong Wu Yong Liu Chunyan Miao Binqiang Zhao Yin Zhao and Lu Guan. 2019. PD-GAN: Adversarial learning for personalized diversity-promoting recommendation. In IJCAI. 3870--3876.","DOI":"10.24963\/ijcai.2019\/537"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Yao Wu Christopher DuBois Alice X Zheng and Martin Ester. 2016. Collaborative denoising auto-encoders for top-N recommender systems. In WSDM. 153--162.","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Zihao Wu Xin Wang Hong Chen Kaidong Li Yi Han Lifeng Sun and Wenwu Zhu. 2023. Diff4Rec: Sequential recommendation with curriculum-scheduled diffusion augmentation. In MM. 9329--9335.","DOI":"10.1145\/3581783.3612709"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Jheng-Hong Yang Chih-Ming Chen Chuan-Ju Wang and Ming-Feng Tsai. 2018. HOP-Rec: High-order proximity for implicit recommendation. In RecSys. 140--144.","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_2_1_50_1","unstructured":"Zhengyi Yang Jiancan Wu Zhicai Wang Xiang Wang Yancheng Yuan and Xiangnan He. 2023. Generate what you prefer: Reshaping sequential recommendation via guided diffusion. In NeurIPS."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Guorui Zhou Xiaoqiang Zhu Chenru Song Ying Fan Han Zhu Xiao Ma Yanghui Yan Junqi Jin Han Li and Kun Gai. 2018. Deep interest network for click-through rate prediction. In KDD. 1059--1068.","DOI":"10.1145\/3219819.3219823"}],"event":{"name":"SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Washington DC USA","acronym":"SIGIR 2024","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657742","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626772.3657742","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:29:33Z","timestamp":1755840573000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":51,"alternative-id":["10.1145\/3626772.3657742","10.1145\/3626772"],"URL":"https:\/\/doi.org\/10.1145\/3626772.3657742","relation":{},"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"2024-07-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}