{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:15:36Z","timestamp":1767964536502,"version":"3.49.0"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"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":["U1936217, 62172136, 61806035, 62072166, and 61702560"],"award-info":[{"award-number":["U1936217, 62172136, 61806035, 62072166, and 61702560"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research and Technology Development Projects of Anhui Province","award":["202004a5020043"],"award-info":[{"award-number":["202004a5020043"]}]},{"name":"Science and Technology Plan of Hunan Province","award":["2018JJ3691"],"award-info":[{"award-number":["2018JJ3691"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user\u2013item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.<\/jats:p>","DOI":"10.1145\/3466641","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T04:37:30Z","timestamp":1632803850000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation"],"prefix":"10.1145","volume":"40","author":[{"given":"Chengyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1029-9280","authenticated-orcid":false,"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology; Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-261X","authenticated-orcid":false,"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 5th ACM RecSys Workshop on Recommender Systems and the Social Web Co-located with the 7th ACM Conference on Recommender Systems (RecSys\u201913)","volume":"1066","author":"Robin","unstructured":"Robin D. Burke and Fatemeh Vahedian. 2013. Social web recommendation using metapaths . In Proceedings of the 5th ACM RecSys Workshop on Recommender Systems and the Social Web Co-located with the 7th ACM Conference on Recommender Systems (RecSys\u201913) , Bamshad Mobasher, Dietmar Jannach, Werner Geyer, Jill Freyne, Andreas Hotho, Sarabjot Singh Anand, and Ido Guy (Eds.) , Vol. 1066 . ACM, New York, NY. Robin D. Burke and Fatemeh Vahedian. 2013. Social web recommendation using metapaths. In Proceedings of the 5th ACM RecSys Workshop on Recommender Systems and the Social Web Co-located with the 7th ACM Conference on Recommender Systems (RecSys\u201913), Bamshad Mobasher, Dietmar Jannach, Werner Geyer, Jill Freyne, Andreas Hotho, Sarabjot Singh Anand, and Ido Guy (Eds.), Vol. 1066. ACM, New York, NY."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2893875"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331196"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411754"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372154"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864721"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_32"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3439729"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367032.3367224"},{"key":"e_1_2_1_12_1","unstructured":"Wenqi Fan Qing Li and Min Cheng. 2018. Deep modeling of social relations for recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18) the 30th Innovative Applications of Artificial Intelligence (IAAI-18) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press 8075\u20138076.  Wenqi Fan Qing Li and Min Cheng. 2018. Deep modeling of social relations for recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18) the 30th Innovative Applications of Artificial Intelligence (IAAI-18) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press 8075\u20138076."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347011"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04182-3_10"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2641564"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.09.013"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209981"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2831682"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219965"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2014.2381611"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3382180"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372278.3390715"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD\u201920)","author":"Jin Jiarui","unstructured":"Jiarui Jin , Jiarui Qin , Yuchen Fang , Kounianhua Du , Weinan Zhang , Yong Yu , Zheng Zhang , and Alexander J. Smola . 2020. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph . In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD\u201920) , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 75\u201384. Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, and Alexander J. Smola. 2020. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD\u201920), Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 75\u201384."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3346970"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772927"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357898"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972757.43"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.09.019"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46295-0_23"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCIS.2018.8691240"},{"key":"e_1_2_1_38_1","unstructured":"Chun-Yi Liu Chuan Zhou Jia Wu Yue Hu and Li Guo. 2018. Social recommendation with an essential preference space. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918) the 30th Innovative Applications of Artificial Intelligence (IAAI\u201918) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201918) Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press 346\u2013353.  Chun-Yi Liu Chuan Zhou Jia Wu Yue Hu and Li Guo. 2018. Social recommendation with an essential preference space. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918) the 30th Innovative Applications of Artificial Intelligence (IAAI\u201918) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201918) Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press 346\u2013353."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2013.04.002"},{"key":"e_1_2_1_40_1","article-title":"Modelling high-order social relations for item recommendation","volume":"10","author":"Liu Yang","year":"2020","unstructured":"Yang Liu , Chen Liang , Xiangnan He , Jiaying Peng , Zibin Zheng , and Jie Tang . 2020 . Modelling high-order social relations for item recommendation . IEEE Transactions on Knowledge and Data Engineering.DOI : 10 .1109\/TKDE.2020.3039463 10.1109\/TKDE.2020.3039463 Yang Liu, Chen Liang, Xiangnan He, Jiaying Peng, Zibin Zheng, and Jie Tang. 2020. Modelling high-order social relations for item recommendation. IEEE Transactions on Knowledge and Data Engineering.DOI:10.1109\/TKDE.2020.3039463","journal-title":"IEEE Transactions on Knowledge and Data Engineering.DOI"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1571978"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458205"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042573.3042664"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3326937.3341257"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331313"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/2981562.2981720"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806528"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2018.00039"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0141-9"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 430\u2013446","author":"Vijaikumar M","year":"2019","unstructured":"M Vijaikumar , Shirish Shevade , and M Narasimha Murty . 2019 . SoRecGAT: Leveraging graph attention mechanism for top-n social recommendation . In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 430\u2013446 . M Vijaikumar, Shirish Shevade, and M Narasimha Murty. 2019. SoRecGAT: Leveraging graph attention mechanism for top-n social recommendation. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 430\u2013446."},{"key":"e_1_2_1_54_1","volume-title":"Graphgan: Graph representation learning with generative adversarial nets. arXiv:1711.08267.","author":"Wang Hongwei","year":"2017","unstructured":"Hongwei Wang , Jia Wang , Jialin Wang , Miao Zhao , Weinan Zhang , Fuzheng Zhang , Xing Xie , and Minyi Guo . 2017 . Graphgan: Graph representation learning with generative adversarial nets. arXiv:1711.08267. Retrieved from https:\/\/arxiv.org\/abs\/1711.08267. Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2017. Graphgan: Graph representation learning with generative adversarial nets. arXiv:1711.08267. Retrieved from https:\/\/arxiv.org\/abs\/1711.08267."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2010.2055045"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_9"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3440248"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2655449"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106344"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367579"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313442"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988925"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663356"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00087"},{"key":"e_1_2_1_67_1","unstructured":"Junliang Yu Hongzhi Yin Jundong Li Min Gao Zi Huang and Lizhen Cui. 2020. Enhance social recommendation with adversarial graph convolutional networks. arXiv:2004.02340. Retrieved from https:\/\/arxiv.org\/abs\/2004.02340.  Junliang Yu Hongzhi Yin Jundong Li Min Gao Zi Huang and Lizhen Cui. 2020. Enhance social recommendation with adversarial graph convolutional networks. arXiv:2004.02340. Retrieved from https:\/\/arxiv.org\/abs\/2004.02340."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458750"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367634"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098063"},{"key":"e_1_2_1_71_1","volume-title":"Proceedings of the International Conference on Information Systems (ICIS\u201911)","author":"Zheng YiMing","unstructured":"YiMing Zheng , Kexin Zhao , and Antonis C. Stylianou . 2011. The formation of social influence in online recommendation systems: A study of user reviews on amazon.com . In Proceedings of the International Conference on Information Systems (ICIS\u201911) , Dennis F. Galletta and Ting-Peng Liang (Eds.). Association for Information Systems. YiMing Zheng, Kexin Zhao, and Antonis C. Stylianou. 2011. The formation of social influence in online recommendation systems: A study of user reviews on amazon.com. In Proceedings of the International Conference on Information Systems (ICIS\u201911), Dennis F. Galletta and Ting-Peng Liang (Eds.). Association for Information Systems."},{"key":"e_1_2_1_72_1","unstructured":"Jie Zhou Ganqu Cui Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv:1812.08434. Retrieved from https:\/\/arxiv.org\/abs\/1812.08434.  Jie Zhou Ganqu Cui Zhengyan Zhang Cheng Yang Zhiyuan Liu Lifeng Wang Changcheng Li and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv:1812.08434. Retrieved from https:\/\/arxiv.org\/abs\/1812.08434."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357805"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3466641","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3466641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:52Z","timestamp":1750195492000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3466641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":73,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4,30]]}},"alternative-id":["10.1145\/3466641"],"URL":"https:\/\/doi.org\/10.1145\/3466641","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]},"assertion":[{"value":"2020-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}