{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:17:02Z","timestamp":1781587022395,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62276196"],"award-info":[{"award-number":["No.62276196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761244","type":"proceedings-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T23:55:33Z","timestamp":1762559733000},"page":"1593-1602","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7553-6916","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"Wuhan University of Technology, WuHan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3468-9884","authenticated-orcid":false,"given":"Chunyang","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, WuHan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5976-5947","authenticated-orcid":false,"given":"Yu","family":"Yin","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, WuHan, China and Huawei Technologies Co., Ltd, ShangHai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-077X","authenticated-orcid":false,"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Southern Queensland, Springfield, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-3263","authenticated-orcid":false,"given":"Jianwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Iwate University, Morioka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio, Nicholas L\u00e9onard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539452"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_3_2_1_4_1","volume-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to 1 or-1. arXiv preprint arXiv:1602.02830","author":"Courbariaux Matthieu","year":"2016","unstructured":"Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to 1 or-1. arXiv preprint arXiv:1602.02830 (2016)."},{"key":"e_1_3_2_1_5_1","volume-title":"LEARNED STEP SIZE QUANTIZATION. In International Conference on Learning Representations.","author":"Esser Steven K","unstructured":"Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha. [n.d.]. LEARNED STEP SIZE QUANTIZATION. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI50040.2020.00198"},{"key":"e_1_3_2_1_7_1","volume-title":"OPTQ: Accurate Quantization for Generative Pre-trained Transformers","author":"Frantar E","year":"2023","unstructured":"E Frantar, S Ashkboos, T Hoefler, and D Alistarh. [n.d.]. OPTQ: Accurate Quantization for Generative Pre-trained Transformers. 2023. In URL https:\/\/openreview.net\/forum."},{"key":"e_1_3_2_1_8_1","volume-title":"Low-power computer vision","author":"Gholami Amir","unstructured":"Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. 2022. A survey of quantization methods for efficient neural network inference. In Low-power computer vision. Chapman and Hall\/CRC, 291-326."},{"key":"e_1_3_2_1_9_1","volume-title":"Neuhoff","author":"Gray Robert M.","year":"1998","unstructured":"Robert M. Gray and David L. Neuhoff. 1998. Quantization. IEEE transactions on information theory, Vol. 44, 6 (1998), 2325-2383."},{"key":"e_1_3_2_1_10_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis)","author":"Maxwell Harper F","year":"2015","unstructured":"F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1-19."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_1_13_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657857"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3643859"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637841"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657820"},{"key":"e_1_3_2_1_19_1","first-page":"87","article-title":"Awq: Activation-aware weight quantization for on-device llm compression and acceleration","volume":"6","author":"Lin Ji","year":"2024","unstructured":"Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. 2024. Awq: Activation-aware weight quantization for on-device llm compression and acceleration. Proceedings of Machine Learning and Systems, Vol. 6 (2024), 87-100.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00489"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i13.29336"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112934"},{"key":"e_1_3_2_1_23_1","unstructured":"Paulius Micikevicius Dusan Stosic Neil Burgess Marius Cornea Pradeep Dubey Richard Grisenthwaite Sangwon Ha Alexander Heinecke Patrick Judd John Kamalu et al. 2022. Fp8 formats for deep learning. arXiv preprint arXiv:2209.05433 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2019EDP7258"},{"key":"e_1_3_2_1_25_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)."},{"key":"e_1_3_2_1_26_1","unstructured":"Wenqi Shao Mengzhao Chen Zhaoyang Zhang Peng Xu Lirui Zhao Zhiqian Li Kaipeng Zhang Peng Gao Yu Qiao and Ping Luo. 2024. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models. In ICLR."},{"key":"e_1_3_2_1_27_1","volume-title":"Attention is all you need. Advances in neural information processing systems","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, Vol. 30 (2017)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271784"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_1_31_1","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022a. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 5 (2022), 4425-4445.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Machine Learning. PMLR, 38087-38099","author":"Xiao Guangxuan","year":"2023","unstructured":"Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. 2023. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning. PMLR, 38087-38099."},{"key":"e_1_3_2_1_35_1","first-page":"787","article-title":"Hyper meta-path contrastive learning for multi-behavior recommendation. In 2021 ieee international conference on data mining (icdm)","author":"Yang Haoran","year":"2021","unstructured":"Haoran Yang, Hongxu Chen, Lin Li, Philip S Yu, and Guandong Xu. 2021. Hyper meta-path contrastive learning for multi-behavior recommendation. In 2021 ieee international conference on data mining (icdm). IEEE, 787-796.","journal-title":"IEEE"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMC2-NIPS53020.2019.00016"},{"key":"e_1_3_2_1_37_1","unstructured":"Zeyu Zhu Fanrong Li Zitao Mo Qinghao Hu Gang Li Zejian Liu Xiaoyao Liang and Jian Cheng. 2023. A2Q: Aggregation-Aware Quantization for Graph Neural Networks. In ICLR."}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","location":"Seoul Republic of Korea","acronym":"CIKM '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:42Z","timestamp":1765497642000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":37,"alternative-id":["10.1145\/3746252.3761244","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761244","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}