{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:53:26Z","timestamp":1775217206894,"version":"3.50.1"},"reference-count":89,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"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":["61976001, 72101176"],"award-info":[{"award-number":["61976001, 72101176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Projects of University Excellent Talents Support Plan of Anhui Provincial Department of Education","award":["gxyqZD2021089"],"award-info":[{"award-number":["gxyqZD2021089"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and informative. However, most current multi-behavior recommendation methods neglect the exploration of social relations between users. Actually, users\u2019 potential social connections are critical to assist them in filtering multifarious messages, which may be one key for models to tap deeper into users\u2019 interests. Additionally, existing models usually focus on the positive behaviors (e.g.,\n            <jats:italic>click<\/jats:italic>\n            ,\n            <jats:italic>follow<\/jats:italic>\n            , and\n            <jats:italic>purchase<\/jats:italic>\n            ) of users and tend to ignore the value of negative behaviors (e.g.,\n            <jats:italic>unfollow<\/jats:italic>\n            and\n            <jats:italic>badpost<\/jats:italic>\n            ). In this work, we present a Multi-Behavior Graph (MBG) construction method based on user behaviors and social relationships and then introduce a novel socially enhanced and behavior-aware graph neural network for behavior prediction. Specifically, we propose a\n            <jats:italic>Socially Enhanced Heterogeneous Graph Convolutional Network<\/jats:italic>\n            (SHGCN) model, which utilizes behavior heterogeneous graph convolution module and social graph convolution module to effectively incorporate behavior features and social information to achieve precise multi-behavior prediction. In addition, the aggregation pooling mechanism is suggested to integrate the outputs of different graph convolution layers, and a dynamic adaptive loss (DAL) method is presented to explore the weight of each behavior. The experimental results on the datasets of the e-commerce platforms (i.e., Epinions and Ciao) indicate the promising performance of SHGCN. Compared with the most powerful baseline, SHGCN achieves 3.3% and 1.4% uplift in terms of\n            <jats:italic>AUC<\/jats:italic>\n            on the Epinions and Ciao datasets. Further experiments, including model efficiency analysis, DAL mechanism, and ablation experiments, confirm the validity of the multi-behavior information and social enhancement.\n          <\/jats:p>","DOI":"10.1145\/3617510","type":"journal-article","created":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T10:38:05Z","timestamp":1693046285000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior Prediction"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6447-2053","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Department of Computer Science and Technology, Anhui University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2657-7720","authenticated-orcid":false,"given":"Wuji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Department of Computer Science and Technology, Anhui University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4929-8587","authenticated-orcid":false,"given":"Likang","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1870-7472","authenticated-orcid":false,"given":"Ming","family":"He","sequence":"additional","affiliation":[{"name":"AI Lab, Lenovo Research; Department of Electronic Engineering, Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-4803","authenticated-orcid":false,"given":"Hongke","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Tianjin University; Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"4502","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Battaglia Peter W.","year":"2016","unstructured":"Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu. 2016. Interaction networks for learning about objects, relations and physics. In Annual Conference on Neural Information Processing Systems, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 4502\u20134510."},{"key":"e_1_3_2_3_2","article-title":"User cold-start recommendation via inductive heterogeneous graph neural network","author":"Cai Desheng","year":"2022","unstructured":"Desheng Cai, Shengsheng Qian, Quan Fang, Jun Hu, and Changsheng Xu. 2022. User cold-start recommendation via inductive heterogeneous graph neural network. ACM Trans. Inf. Syst. 41, 3 (2022).","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_4_2","volume-title":"5th International Conference on Learning Representations","author":"Chang Michael","year":"2017","unstructured":"Michael Chang, Tomer D. Ullman, Antonio Torralba, and Joshua B. Tenenbaum. 2017. A compositional object-based approach to learning physical dynamics. In 5th International Conference on Learning Representations. OpenReview. net."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_2_6_2","first-page":"794","volume-title":"International Conference on Machine Learning","author":"Chen Zhao","year":"2018","unstructured":"Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. 2018. GradNorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International Conference on Machine Learning. PMLR, 794\u2013803."},{"key":"e_1_3_2_7_2","article-title":"Multi-task learning with deep neural networks: A survey","volume":"2009","author":"Crawshaw Michael","year":"2020","unstructured":"Michael Crawshaw. 2020. Multi-task learning with deep neural networks: A survey. CoRR abs\/2009.09796 (2020).","journal-title":"CoRR"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/464"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Ziniu Hu Kuansan Wang Yizhou Sun and Jie Tang. 2020. Heterogeneous network representation learning.International Joint Conferences on Artificial Intelligence Organization (IJCAI). 20 (2020) 4861\u20134867.","DOI":"10.24963\/ijcai.2020\/677"},{"key":"e_1_3_2_10_2","first-page":"2224","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Duvenaud David","year":"2015","unstructured":"David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael G\u00f3mez-Bombarelli, Timothy Hirzel, Al\u00e1n Aspuru-Guzik, and Ryan P. Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Annual Conference on Neural Information Processing Systems, Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett (Eds.). 2224\u20132232."},{"key":"e_1_3_2_11_2","first-page":"1263","volume-title":"34th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"70","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 1263\u20131272."},{"key":"e_1_3_2_12_2","article-title":"Metrics for multi-class classification: An overview","author":"Grandini Margherita","year":"2020","unstructured":"Margherita Grandini, Enrico Bagli, and Giorgio Visani. 2020. Metrics for multi-class classification: An overview. arXiv preprint arXiv:2008.05756 (2020).","journal-title":"arXiv preprint arXiv:2008.05756"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.12.007"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330670"},{"key":"e_1_3_2_16_2","first-page":"1024","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Annual Conference on Neural Information Processing Systems. 1024\u20131034."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330838"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_19_2","first-page":"1","volume-title":"International Joint Conference on Neural Networks","author":"Huang Xinting","year":"2019","unstructured":"Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang, and Hai-Tao Zheng. 2019. CARL: Aggregated search with context-aware module embedding learning. In International Joint Conference on Neural Networks. IEEE, 1\u20138."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539350"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00133"},{"key":"e_1_3_2_22_2","first-page":"547","article-title":"\u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura","volume":"37","author":"Jaccard Paul","year":"1901","unstructured":"Paul Jaccard. 1901. \u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull. Soc. Vaudoise Sci. Nat. 37 (1901), 547\u2013579.","journal-title":"Bull. Soc. Vaudoise Sci. Nat."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_24_2","first-page":"7482","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Kendall Alex","year":"2018","unstructured":"Alex Kendall, Yarin Gal, and Roberto Cipolla. 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In IEEE Conference on Computer Vision and Pattern Recognition. 7482\u20137491."},{"key":"e_1_3_2_25_2","volume-title":"3rd International Conference on Learning Representations","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_26_2","volume-title":"5th International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124317"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/1352793.1352837"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357951"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220014"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.20591"},{"key":"e_1_3_2_32_2","first-page":"519","volume-title":"International Joint Conference on Artificial Intelligence","author":"Ling Charles X.","year":"2003","unstructured":"Charles X. Ling, Jin Huang, and Harry Zhang. 2003. AUC: A statistically consistent and more discriminating measure than accuracy. In International Joint Conference on Artificial Intelligence. 519\u2013524."},{"key":"e_1_3_2_33_2","article-title":"BehaviorNet: A fine-grained behavior-aware network for dynamic link prediction","author":"Liu Mingyi","year":"2023","unstructured":"Mingyi Liu, Zhiying Tu, Tonghua Su, Xianzhi Wang, Xiaofei Xu, and Zhongjie Wang. 2023. BehaviorNet: A fine-grained behavior-aware network for dynamic link prediction. ACM Trans. Web (2023).","journal-title":"ACM Trans. Web"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00197"},{"issue":"4","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501815","article-title":"Federated social recommendation with graph neural network","volume":"13","author":"Liu Zhiwei","year":"2022","unstructured":"Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S. Yu. 2022. Federated social recommendation with graph neural network. ACM Trans. Intell. Syst. Technol. 13, 4 (2022), 1\u201324.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959163"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1177\/0049124193022001006"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401098"},{"key":"e_1_3_2_39_2","volume-title":"1st International Conference on Learning Representations","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations."},{"key":"e_1_3_2_40_2","first-page":"1","volume-title":"Symposium on Machine Learning in Speech and Language Processing","author":"Moore Robert","year":"2011","unstructured":"Robert Moore and John DeNero. 2011. L1 and L2 regularization for multiclass hinge loss models. In Symposium on Machine Learning in Speech and Language Processing. ISCA, 1\u20135."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403393"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462879"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463074"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1177\/1350507616680563"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/564376.564421"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16561"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_50_2","article-title":"Graph convolutional matrix completion","volume":"1706","author":"Berg Rianne van den","year":"2017","unstructured":"Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph convolutional matrix completion. CoRR abs\/1706.02263 (2017).","journal-title":"CoRR"},{"key":"e_1_3_2_51_2","volume-title":"6th International Conference on Learning Representations","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In 6th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_52_2","article-title":"Sequential recommendation with multiple contrast signals","author":"Wang Chenyang","year":"2022","unstructured":"Chenyang Wang, Weizhi Ma, and Chong Chen. 2022. Sequential recommendation with multiple contrast signals. ACM Trans. Inf. Syst. 41, 1 (2022).","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580511"},{"key":"e_1_3_2_55_2","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019).","journal-title":"arXiv preprint arXiv:1909.01315"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380077"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498527"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498527"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00126-0_16"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401443"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00183"},{"key":"e_1_3_2_65_2","article-title":"Exploring large language model for graph data understanding in online job recommendations","author":"Wu Likang","year":"2023","unstructured":"Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu, and Enhong Chen. 2023. Exploring large language model for graph data understanding in online job recommendations. arXiv preprint arXiv:2307.05722 (2023).","journal-title":"arXiv preprint arXiv:2307.05722"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00179"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00179"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401445"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331203"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772790"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467191"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331188"},{"key":"e_1_3_2_76_2","volume-title":"7th International Conference on Learning Representations","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In 7th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570386"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00090"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463028"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3023787"},{"key":"e_1_3_2_81_2","first-page":"11960","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019. Graph transformer networks. In Annual Conference on Neural Information Processing Systems, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d\u2019Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 11960\u201311970."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_3_2_84_2","article-title":"A community division-based evolutionary algorithm for large-scale multi-objective recommendations","author":"Zhang Lei","year":"2022","unstructured":"Lei Zhang, Huabin Zhang, Sibo Liu, Chao Wang, and Hongke Zhao. 2022. A community division-based evolutionary algorithm for large-scale multi-objective recommendations. IEEE Trans. Emerg. Topics Comput. Intell. (2022).","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"e_1_3_2_85_2","article-title":"Knowledge-enhanced attributed multi-task learning for medicine recommendation","author":"Zhang Yingying","year":"2022","unstructured":"Yingying Zhang, Xian Wu, Quan Fang, Shengsheng Qian, and Chengsheng Xu. 2022. Knowledge-enhanced attributed multi-task learning for medicine recommendation. ACM Trans. Inf. Syst. 41, 1 (2022).","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_86_2","first-page":"887","article-title":"Cross-domain recommendation via user interest alignment","author":"Zhao Chuang","year":"2023","unstructured":"Chuang Zhao, Hongke Zhao, Ming He, Jian Zhang, and Jianping Fan. 2023. Cross-domain recommendation via user interest alignment. In ACM Web Conference. 887\u2013896.","journal-title":"ACM Web Conference"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2906199"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741656"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3577032"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580517"}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617510","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3617510","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:45:58Z","timestamp":1750178758000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,11]]},"references-count":89,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3617510"],"URL":"https:\/\/doi.org\/10.1145\/3617510","relation":{},"ISSN":["1559-1131","1559-114X"],"issn-type":[{"value":"1559-1131","type":"print"},{"value":"1559-114X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,11]]},"assertion":[{"value":"2022-12-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}