{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:54:51Z","timestamp":1773510891018,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the STI 2030-Major Projects","award":["2021ZD0201404"],"award-info":[{"award-number":["2021ZD0201404"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709278","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:42:22Z","timestamp":1743792142000},"page":"1891-1902","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9506-0477","authenticated-orcid":false,"given":"Chenhao","family":"Zhai","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2914-6527","authenticated-orcid":false,"given":"Chang","family":"Meng","sequence":"additional","affiliation":[{"name":"Kuaishou Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6079-3549","authenticated-orcid":false,"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2678-8556","authenticated-orcid":false,"given":"Kexin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7120-4857","authenticated-orcid":false,"given":"Xuhao","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-1923","authenticated-orcid":false,"given":"Xiu","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"12th USENIX symposium on operating systems design and implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265--283."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474237"},{"key":"e_1_3_2_2_3_1","volume-title":"Multitask learning. Machine learning","author":"Caruana Rich","year":"1997","unstructured":"Rich Caruana. 1997. Multitask learning. Machine learning, Vol. 28, 1 (1997), 41--75."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583439"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25540"},{"key":"e_1_3_2_2_8_1","volume-title":"Augmenting Sequential Recommendation with Balanced Relevance and Diversity. arXiv preprint arXiv:2412.08300","author":"Dang Yizhou","year":"2024","unstructured":"Yizhou Dang, Jiahui Zhang, Yuting Liu, Enneng Yang, Yuliang Liang, Guibing Guo, Jianzhe Zhao, and Xingwei Wang. 2024. Augmenting Sequential Recommendation with Balanced Relevance and Diversity. arXiv preprint arXiv:2412.08300 (2024)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Jingtao Ding Guanghui Yu Xiangnan He Yuhan Quan Yong Li Tat-Seng Chua Depeng Jin and Jiajie Yu. 2018. Improving Implicit Recommender Systems with View Data.. In IJCAI. 3343--3349.","DOI":"10.24963\/ijcai.2018\/464"},{"key":"e_1_3_2_2_10_1","volume-title":"Neural multi-task recommendation from multi-behavior data","author":"Gao Chen","unstructured":"Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, and Depeng Jin. 2019. Neural multi-task recommendation from multi-behavior data. In ICDE. IEEE, 1554--1557."},{"key":"e_1_3_2_2_11_1","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/285"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.005"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Long Guo Lifeng Hua Rongfei Jia Binqiang Zhao Xiaobo Wang and Bin Cui. 2019. Buying or browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior. In SIGKDD. 1984--1992.","DOI":"10.1145\/3292500.3330670"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583312"},{"key":"e_1_3_2_2_16_1","volume-title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. arXiv preprint arXiv:2002.02126","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. arXiv preprint arXiv:2002.02126 (2020)."},{"key":"e_1_3_2_2_17_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_2_18_1","volume-title":"A Survey on User Behavior Modeling in Recommender Systems. arXiv preprint arXiv:2302.11087","author":"He Zhicheng","year":"2023","unstructured":"Zhicheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, and Ruiming Tang. 2023. A Survey on User Behavior Modeling in Recommender Systems. arXiv preprint arXiv:2302.11087 (2023)."},{"key":"e_1_3_2_2_19_1","volume-title":"Recent Advances in Heterogeneous Relation Learning for Recommendation. arXiv preprint arXiv:2110.03455","author":"Huang Chao","year":"2021","unstructured":"Chao Huang. 2021. Recent Advances in Heterogeneous Relation Learning for Recommendation. arXiv preprint arXiv:2110.03455 (2021)."},{"key":"e_1_3_2_2_20_1","volume-title":"Adaptive mixtures of local experts. Neural computation","author":"Jacobs Robert A","year":"1991","unstructured":"Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation, Vol. 3, 1 (1991), 79--87."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Bowen Jin Chen Gao Xiangnan He Depeng Jin and Yong Li. 2020. Multi-behavior recommendation with graph convolutional networks. In SIGIR.","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_2_22_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124317"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412713"},{"key":"e_1_3_2_2_25_1","volume-title":"Dual-Channel Multiplex Graph Neural Networks for Recommendation. arXiv preprint arXiv:2403.11624","author":"Li Xiang","year":"2024","unstructured":"Xiang Li, Chaofan Fu, Zhongying Zhao, Guanjie Zheng, Chao Huang, Junyu Dong, and Yanwei Yu. 2024a. Dual-Channel Multiplex Graph Neural Networks for Recommendation. arXiv preprint arXiv:2403.11624 (2024)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635794"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282989"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959163"},{"key":"e_1_3_2_2_29_1","volume-title":"H Chi","author":"Ma Jiaqi","year":"2018","unstructured":"Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In SIGKDD. 1930--1939."},{"key":"e_1_3_2_2_30_1","volume-title":"Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation. arXiv preprint arXiv:2410.16755","author":"Meng Chang","year":"2024","unstructured":"Chang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu, Xiaoqiang Feng, Lantao Hu, Xiu Li, Han Li, and Kun Gai. 2024. Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation. arXiv preprint arXiv:2410.16755 (2024)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615004"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599838"},{"key":"e_1_3_2_2_33_1","volume-title":"Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation. arXiv preprint arXiv:2208.01849","author":"Meng Chang","year":"2022","unstructured":"Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang, Haolun Wu, Chen Gao, Dong Li, Xiu Li, and Ruiming Tang. 2022. Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation. arXiv preprint arXiv:2208.01849 (2022)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.04.027"},{"key":"e_1_3_2_2_35_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_2_36_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593--607."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401969"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28749"},{"key":"e_1_3_2_2_39_1","volume-title":"A survey of collaborative filtering techniques. Advances in artificial intelligence","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Hongyan Tang Junning Liu Ming Zhao and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In RecSys. 269--278.","DOI":"10.1145\/3383313.3412236"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939690"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Lisa Torrey and Jude Shavlik. 2010. Transfer learning. In Handbook of research on machine learning applications and trends: algorithms methods and techniques. IGI global 242--264.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"e_1_3_2_2_43_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_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671506"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498527"},{"key":"e_1_3_2_2_46_1","volume-title":"Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling","author":"Xia Lianghao","year":"1931","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, and Liefeng Bo. 2021a. Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling. In ICDE. IEEE, 1931--1936."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Chao Huang Yong Xu Peng Dai Bo Zhang and Liefeng Bo. 2020. Multiplex behavioral relation learning for recommendation via memory augmented transformer network. In SIGIR. 2397--2406.","DOI":"10.1145\/3397271.3401445"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Yong Xu Chao Huang Peng Dai and Liefeng Bo. 2021c. Graph meta network for multi-behavior recommendation. In SIGIR. 757--766.","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570386"},{"key":"e_1_3_2_2_51_1","volume-title":"Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation. arXiv preprint arXiv:2205.13128","author":"Yan Mingshi","year":"2022","unstructured":"Mingshi Yan, Zhiyong Cheng, Chen Gao, Jing Sun, Fan Liu, Fuming Sun, and Haojie Li. 2022. Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation. arXiv preprint arXiv:2205.13128 (2022)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657696"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126034"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615074"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741656"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11618"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709278","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:37:19Z","timestamp":1755358639000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":56,"alternative-id":["10.1145\/3690624.3709278","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709278","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}