{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:38:04Z","timestamp":1775799484777,"version":"3.50.1"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62222213, 62072423"],"award-info":[{"award-number":["62222213, 62072423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            A well-developed recommendation system can not only leverage multi-typed interactions (such as\n            <jats:italic>page view<\/jats:italic>\n            ,\n            <jats:italic>add-to-cart<\/jats:italic>\n            , and\n            <jats:italic>purchase<\/jats:italic>\n            ) to better identify user preferences but also demonstrate high performance, low complexity, and strong interpretability. However, many existing solutions for multi-behavior recommendation fall short of intuitive modeling of real-world scenarios, leading to overly complex models with massive parameters and cumbersome components. In particular, they share two critical limitations: (1) Some pioneering models are built upon the strict assumption of cascade effects across behaviors, which contradicts multifarious behavior paths in practical applications. (2) Existing approaches fail to explicitly capture the unique idiosyncrasies of users and even neglect the inherent nature of items involved in the multi-behavior interactions. To this end, we propose a novel Directed Acyclic Graph Convolutional Network (DA-GCN) for the multi-behavior recommendation task. Specifically, we pinpoint the partial order relations within the monotonic behavior chain and extend it to personalized directed acyclic behavior graphs to exploit behavior dependencies. Then, a GCN-based directed edge encoder is employed to distill rich collaborative signals embodied by each directed edge. In light of the information flows over the directed acyclic structure, we propose an attentive aggregation module to gather messages from all potential antecedent behaviors, representing distinct perspectives to understand the terminated behavior. Thus, we obtain comprehensive representations for the follow-up behavior through learnable distributions over its preceding behaviors, explicitly reflecting personalized interactive patterns of users and underlying properties of items simultaneously. Finally, we design a customized multi-task learning objective for flexible joint optimization. Extensive experiments on public benchmarking datasets fully demonstrate the superiority of DA-GCN with significant performance improvement and computational efficiency over a wide range of state-of-the-art methods. Our code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/xizhu1022\/DA-GCN\">https:\/\/github.com\/xizhu1022\/DA-GCN<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3696417","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T15:55:08Z","timestamp":1726761308000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior Graphs"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3621-8493","authenticated-orcid":false,"given":"Xi","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1402-2358","authenticated-orcid":false,"given":"Fake","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0814-7313","authenticated-orcid":false,"given":"Ziwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-5386","authenticated-orcid":false,"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4597-9227","authenticated-orcid":false,"given":"Zikai","family":"Yin","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3699-0697","authenticated-orcid":false,"given":"Xueying","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"2787","volume-title":"Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garc\u00eda-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-Relational Data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Christopher J. C. Burges, L\u00e9on Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger (Eds.), 2787\u20132795. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2013\/hash\/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html"},{"key":"e_1_3_2_3_2","first-page":"3958","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), The 11th Symposium on Educational Advances in Artificial Intelligence (EAAI \u201921)","author":"Chen Chong","year":"2021","unstructured":"Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu, and Shaoping Ma. 2021. Graph Heterogeneous Multi-Relational Recommendation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), The 11th Symposium on Educational Advances in Artificial Intelligence (EAAI \u201921). AAAI Press, 3958\u20133966. Retrieved from https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16515"},{"key":"e_1_3_2_4_2","first-page":"19","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI \u201920), 32nd Innovative Applications of Artificial Intelligence Conference (IAAI \u201920), 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI \u201920)","author":"Chen Chong","year":"2020","unstructured":"Chong Chen, Min Zhang, Yongfeng Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI \u201920), 32nd Innovative Applications of Artificial Intelligence Conference (IAAI \u201920), 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI \u201920). AAAI Press, 19\u201326. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5329"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Xiaoqing Chen Zhitao Li Weike Pan and Zhong Ming. 2023. A Survey on Multi-Behavior Sequential Recommendation. arXiv:2308.15701. Retrieved from https:\/\/doi.org\/10.48550\/ARXIV.2308.15701","DOI":"10.48550\/ARXIV.2308.15701"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1145\/3543507.3583439","volume-title":"Proceedings of the ACM Web Conference 2023 (WWW \u201923)","author":"Cheng Zhiyong","year":"2023","unstructured":"Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, and Yuxin Peng. 2023. Multi-Behavior Recommendation with Cascading Graph Convolution Networks. In Proceedings of the ACM Web Conference 2023 (WWW \u201923). Ying Ding, Jie Tang, Juan F. Sequeda, Lora Aroyo, Carlos Castillo, and Geert-Jan Houben (Eds.), ACM, 1181\u20131189. DOI: 10.1145\/3543507.3583439"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1145\/3097983.3098036","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Dong Yuxiao","year":"2017","unstructured":"Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 135\u2013144. DOI: 10.1145\/3097983.3098036"},{"key":"e_1_3_2_9_2","first-page":"1554","volume-title":"Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE \u201919)","author":"Gao Chen","year":"2019","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 Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE \u201919). IEEE, 1554\u20131557. DOI: 10.1109\/ICDE.2019.00140"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2958808"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"3103","DOI":"10.1145\/3511808.3557065","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Gong Xudong","year":"2022","unstructured":"Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2022. Real-Time Short Video Recommendation on Mobile Devices. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Mohammad Al Hasan and Li Xiong (Eds.), ACM, 3103\u20133112. DOI: 10.1145\/3511808.3557065"},{"key":"e_1_3_2_12_2","first-page":"2052","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI \u201922)","author":"Gu Shuyun","year":"2022","unstructured":"Shuyun Gu, Xiao Wang, Chuan Shi, and Ding Xiao. 2022. Self-Supervised Graph Neural Networks for Multi-Behavior Recommendation. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI \u201922). Luc De Raedt (Ed.), ijcai.org, 2052\u20132058. DOI: 10.24963\/ijcai.2022\/285"},{"key":"e_1_3_2_13_2","first-page":"639","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920)","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920). Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.), ACM, 639\u2013648. DOI: 10.1145\/3397271.3401063"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939815"},{"key":"e_1_3_2_16_2","first-page":"659","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920)","author":"Jin Bowen","year":"2020","unstructured":"Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020. Multi-Behavior Recommendation with Graph Convolutional Networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920). Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.), ACM, 659\u2013668. DOI: 10.1145\/3397271.3401072"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11704-022-2324-X"},{"key":"e_1_3_2_18_2","first-page":"1770","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024)","author":"Lin Fake","year":"2024","unstructured":"Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, and Enhong Chen. 2024. When Box Meets Graph Neural Network in Tag-Aware Recommendation. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024). Ricardo Baeza-Yates and Francesco Bonchi (Eds.), ACM, 1770\u20131780. DOI: 10.1145\/3637528.3671973"},{"key":"e_1_3_2_19_2","first-page":"1797","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023)","author":"Meng Chang","year":"2023","unstructured":"Chang Meng, Chenhao Zhai, Yu Yang, Hengyu Zhang, and Xiu Li. 2023. Parallel Knowledge Enhancement Based Framework for Multi-Behavior Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023). Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, Min Zhang, and Rodrygo L. T. Santos (Eds.), ACM, 1797\u20131806. DOI: 10.1145\/3583780.3615004"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599838"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3606369"},{"key":"e_1_3_2_22_2","unstructured":"Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G. Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep Learning Recommendation Model for Personalization and Recommendation Systems. arXiv: 1906.00091. Retrieved from http:\/\/arxiv.org\/abs\/1906.00091"},{"key":"e_1_3_2_23_2","first-page":"995","volume-title":"Proceedings of the 10th IEEE International Conference on Data Mining (ICDM \u201910)","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle. 2010. Factorization Machines. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM \u201910). Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.), IEEE Computer Society, 995\u20131000. DOI: 10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_24_2","first-page":"452","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI \u201909)","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 Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI \u201909). Jeff A. Bilmes and Andrew Y. Ng (Eds.), AUAI Press, 452\u2013461. Retrieved from https:\/\/www.auai.org\/uai2009\/papers\/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498513"},{"key":"e_1_3_2_26_2","first-page":"86","volume-title":"Proceedings of the 12th ACM Conference on Recommender Systems (RecSys \u201918)","author":"Wan Mengting","year":"2018","unstructured":"Mengting Wan and Julian J. McAuley. 2018. Item Recommendation on Monotonic Behavior Chains. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys \u201918). Sole Pera, Michael D. Ekstrand, Xavier Amatriain, and John O\u2019Donovan (Eds.), ACM, 86\u201394. DOI: 10.1145\/3240323.3240369"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3177455"},{"key":"e_1_3_2_28_2","first-page":"165","volume-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201919)","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201919). Benjamin Piwowarski, Max Chevalier, \u00c9ric Gaussier, Yoelle Maarek, Jian-Yun Nie, and Falk Scholer (Eds.), ACM, 165\u2013174. DOI: 10.1145\/3331184.3331267"},{"key":"e_1_3_2_29_2","first-page":"1001","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920)","author":"Wang Xiang","year":"2020","unstructured":"Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201920). Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.), ACM, 1001\u20131010. DOI: 10.1145\/3397271.3401137"},{"key":"e_1_3_2_30_2","first-page":"1120","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM \u201922)","author":"Wei Wei","year":"2022","unstructured":"Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022. Contrastive Meta Learning with Behavior Multiplicity for Recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM \u201922). K. Selcuk Candan, Huan Liu, Leman Akoglu, Xin Luna Dong, and Jiliang Tang (Eds.), ACM, 1120\u20131128. DOI: 10.1145\/3488560.3498527"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/S11704-021-0261-8"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401445"},{"key":"e_1_3_2_34_2","first-page":"4486","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), 11th Symposium on Educational Advances in Artificial Intelligence (EAAI 2021)","author":"Xia Lianghao","year":"2021","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. 2021. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), 11th Symposium on Educational Advances in Artificial Intelligence (EAAI 2021). AAAI Press, 4486\u20134493. Retrieved from https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16576"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532058"},{"key":"e_1_3_2_36_2","first-page":"757","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201921)","author":"Xia Lianghao","year":"2021","unstructured":"Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, and Liefeng Bo. 2021. Graph Meta Network for Multi-Behavior Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201921). Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai (Eds.), ACM, 757\u2013766. DOI: 10.1145\/3404835.3462972"},{"key":"e_1_3_2_37_2","first-page":"932","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201923)","author":"Xin Xin","year":"2023","unstructured":"Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M. Jose, Maarten de Rijke, and Zhaochun Ren. 2023. Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201923). Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.), ACM, 932\u2013941. DOI: 10.1145\/3539618.3591697"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591734"},{"key":"e_1_3_2_39_2","first-page":"3173","volume-title":"Proceedings of the ACM on Web Conference 2024 (WWW \u201924).","author":"Xu Wujiang","year":"2024","unstructured":"Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, and Junchi Yan. 2024. Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions. In Proceedings of the ACM on Web Conference 2024 (WWW \u201924). Tat-Seng Chua, Chong-Wah Ngo, Ravi Kumar, Hady W. Lauw, and Roy Ka-Wei Lee (Eds.), ACM, 3173\u20133184. DOI: 10.1145\/3589334.3645351"},{"key":"e_1_3_2_40_2","first-page":"195","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923)","author":"Xuan Hongrui","year":"2023","unstructured":"Hongrui Xuan, Yi Liu, Bohan Li, and Hongzhi Yin. 2023. Knowledge Enhancement for Contrastive Multi-Behavior Recommendation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923). Tat-Seng Chua, Hady W. Lauw, Luo Si, Evimaria Terzi, and Panayiotis Tsaparas (Eds.), ACM, 195\u2013203. DOI: 10.1145\/3539597.3570386"},{"key":"e_1_3_2_41_2","first-page":"1649","volume-title":"Proceedings of the Web Conference 2021 (WWW \u201921)","author":"Xue Hansheng","year":"2021","unstructured":"Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Yi Wei, and Yu Lin. 2021. Multiplex Bipartite Network Embedding Using Dual Hypergraph Convolutional Networks. In Proceedings of the Web Conference 2021 (WWW \u201921). Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.), ACM\/IW3C2, 1649\u20131660. DOI: 10.1145\/3442381.3449954"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3587693"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657696"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481952"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1145\/3219819.3219890","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201918)","author":"Ying Rex","year":"2018","unstructured":"Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201918). Yike Guo and Faisal Farooq (Eds.), ACM, 974\u2013983. DOI: 10.1145\/3219819.3219890"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1145\/3543507.3583513","volume-title":"Proceedings of the ACM Web Conference 2023 (WWW \u201923)","author":"Zhang Chi","year":"2023","unstructured":"Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, and Li Li. 2023. Denoising and Prompt-Tuning for Multi-Behavior Recommendation. In Proceedings of the ACM Web Conference 2023 (WWW \u201923). Ying Ding, Jie Tang, Juan F. Sequeda, Lora Aroyo, Carlos Castillo, and Geert-Jan Houben (Eds.), ACM, 1355\u20131363. DOI: 10.1145\/3543507.3583513"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098063"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591775"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1145\/3219819.3219823","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201918)","author":"Zhou Guorui","year":"2018","unstructured":"Guorui Zhou, Xiaoqiang Zhu, Chengru 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 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201918). Yike Guo and Faisal Farooq (Eds.), ACM, 1059\u20131068. DOI: 10.1145\/3219819.3219823"},{"key":"e_1_3_2_50_2","first-page":"938","volume-title":"Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE \u201919)","author":"Zhou Xiangmin","year":"2019","unstructured":"Xiangmin Zhou, Dong Qin, Xiaolu Lu, Lei Chen, and Yanchun Zhang. 2019. Online Social Media Recommendation Over Streams. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE \u201919). IEEE, 938\u2013949. DOI: 10.1109\/ICDE.2019.00088"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696417","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:55Z","timestamp":1750295935000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3696417"],"URL":"https:\/\/doi.org\/10.1145\/3696417","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]},"assertion":[{"value":"2023-08-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}