{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:48:27Z","timestamp":1772610507209,"version":"3.50.1"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"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":["72301144"],"award-info":[{"award-number":["72301144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"crossref","award":["23KJB520013"],"award-info":[{"award-number":["23KJB520013"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Recommendation system concentrates on quickly matching products to consumer\u2019s needs, which plays a major role in improving user experiences and increase conversion rate. Travel recommendation has become a hot topic in both industry and academia with the development of the tourism industry. Nevertheless, the selection of travel products entails careful consideration of various geographical factors, such as departure and destination. Meanwhile, due to the limitation of finance and time, users browse and purchase travel products less frequently than they do for traditional products, which leads to data sparsity problem in representation learning. To solve these challenges, a novel model named GHGCL (short for\n            <jats:bold>G<\/jats:bold>\n            eography-aware\n            <jats:bold>H<\/jats:bold>\n            eterogeneous\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>C<\/jats:bold>\n            ontrastive\n            <jats:bold>L<\/jats:bold>\n            earning) is proposed for recommending travel products. Concretely, we model the travel recommender system as a heterogeneous information network with geographical information and capture diverse user preferences from local and high-order structures. Especially, we design two kinds of contrastive learning tasks for better user and travel product representation learning. The multi-view contrastive learning aims to bridge the gap between network schema and meta-path view representations. The meta-path contrastive learning focuses on modeling the coarse-grained commonality between different meta-paths from the perspective of different geographical factors, i.e., departure and destination. We assess the performance of GHGCL by performing a series of experiments on a real-world dataset, and the results clearly verify its superiority as compared to the baseline methods.\n          <\/jats:p>","DOI":"10.1145\/3641277","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T12:30:37Z","timestamp":1705926637000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Geography-aware Heterogeneous Graph Contrastive Learning for Travel Recommendation"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5537-8989","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"first","affiliation":[{"name":"Nanjing Forestry University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7049-5614","authenticated-orcid":false,"given":"Jie","family":"Cao","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8035-5255","authenticated-orcid":false,"given":"Weichao","family":"Liang","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong University, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-8610","authenticated-orcid":false,"given":"Qiaolin","family":"Ye","sequence":"additional","affiliation":[{"name":"Nanjing Forestry University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2044016"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372118"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3042507"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487047"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.04.001"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.im.2016.04.003"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Weiwei Jiang and Jiayun Luo. 2022. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications 207 30 (2022) 117921.","DOI":"10.1016\/j.eswa.2022.117921"},{"key":"e_1_3_2_12_2","volume-title":"Proceedings of the 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 Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Xiangjie Kong Zhiqiang Huang Guojiang Shen Hang Lin and Mingjie Lv. 2024. Urban overtourism detection based on graph temporal convolutional networks. IEEE Transactions on Computational Social Systems 11 1 (2024) 1\u201313.","DOI":"10.1109\/TCSS.2022.3226177"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570400"},{"issue":"3","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/TITS.2021.3091534","article-title":"Spatial-temporal deep intention destination networks for online travel planning","volume":"23","author":"Li Yu","year":"2021","unstructured":"Yu Li, Fei Xiong, Ziyi Wang, Zulong Chen, Chuanfei Xu, Yuyu Yin, and Li Zhou. 2021. Spatial-temporal deep intention destination networks for online travel planning. IEEE Trans. Intell. Transport. Syst. 23, 3 (2021), 2688\u20132700.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403252"},{"key":"e_1_3_2_17_2","first-page":"7470","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Lin Haitao","year":"2022","unstructured":"Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, and Stan Z. Li. 2022. Conditional local convolution for spatio-temporal meteorological forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence. 7470\u20137478."},{"key":"e_1_3_2_18_2","first-page":"2320","volume-title":"Proceedings of the World Wide Web Conference","author":"Lin Zihan","year":"2022","unstructured":"Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In Proceedings of the World Wide Web Conference. 2320\u20132329."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110884"},{"key":"e_1_3_2_20_2","first-page":"2177","volume-title":"Proceedings of the World Wide Web Conference","author":"Luo Yingtao","year":"2021","unstructured":"Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-temporal attention network for next location recommendation. In Proceedings of the World Wide Web Conference. 2177\u20132185."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539229"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498505"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_25_2","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. 452\u2013461."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116566"},{"key":"e_1_3_2_28_2","first-page":"8476","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Tao Wanjie","year":"2022","unstructured":"Wanjie Tao, Yu Li, Liangyue Li, Zulong Chen, Hong Wen, Peilin Chen, Tingting Liang, and Quan Lu. 2022. SMINet: State-aware multi-aspect interests representation network for cold-start users recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence. 8476\u20138484."},{"key":"e_1_3_2_29_2","first-page":"1198","volume-title":"Proceedings of the World Wide Web Conference","author":"Taylor Kendall","year":"2018","unstructured":"Kendall Taylor, Kwan Hui Lim, and Jeffrey Chan. 2018. Travel itinerary recommendations with must-see points-of-interest. In Proceedings of the World Wide Web Conference. 1198\u20131205."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/220"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304308"},{"key":"e_1_3_2_32_2","article-title":"A survey on heterogeneous graph embedding: Methods, techniques, applications and sources","author":"Wang Xiao","year":"2022","unstructured":"Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and Philip S. Yu. 2022. A survey on heterogeneous graph embedding: Methods, techniques, applications and sources. IEEE Trans. Big Data 9, 2 (2022), 415\u2013436.","journal-title":"IEEE Trans. Big Data"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475665"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539125"},{"key":"e_1_3_2_39_2","first-page":"3940","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Xu Chengfeng","year":"2019","unstructured":"Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. Graph contextualized self-attention network for session-based recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3940\u20133946."},{"key":"e_1_3_2_40_2","first-page":"204","article-title":"Modeling user preferences on spatiotemporal topics for point-of-interest recommendation","author":"Yang Shuiqiao","year":"2017","unstructured":"Shuiqiao Yang, Guangyan Huang, Yong Xiang, Xiangmin Zhou, and Chi-Hung Chi. 2017. Modeling user preferences on spatiotemporal topics for point-of-interest recommendation. In Proceedings of the IEEE International Conference on Services Computing (SCC\u201917) (2017), 204\u2013211.","journal-title":"Proceedings of the IEEE International Conference on Services Computing (SCC\u201917)"},{"key":"e_1_3_2_41_2","first-page":"3217","volume-title":"Proceedings of the World Wide Web Conference","author":"Yang Zhen","year":"2022","unstructured":"Zhen Yang, Ming Ding, Bin Xu, Hongxia Yang, and Jie Tang. 2022. STAM: A spatiotemporal aggregation method for graph neural network-based recommendation. In Proceedings of the World Wide Web Conference. 3217\u20133228."},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532027"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"issue":"2","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3466641","article-title":"Multi-graph heterogeneous interaction fusion for social recommendation","volume":"40","author":"Zhang Chengyuan","year":"2021","unstructured":"Chengyuan Zhang, Yang Wang, Lei Zhu, Jiayu Song, and Hongzhi Yin. 2021. Multi-graph heterogeneous interaction fusion for social recommendation. ACM Trans. Inf. Syst. 40, 2 (2021), 1\u201326.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_45_2","unstructured":"Mengqi Zhang Shu Wu Xueli Yu Qiang Liu and Liang Wang. 2022. Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering 35 5 (2022) 4741\u20134735."},{"issue":"1","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3462331","article-title":"Contrastive trajectory learning for tour recommendation","volume":"13","author":"Zhou Fan","year":"2021","unstructured":"Fan Zhou, Pengyu Wang, Xovee Xu, Wenxin Tai, and Goce Trajcevski. 2021. Contrastive trajectory learning for tour recommendation. ACM Trans. Intell. Syst. Technol. 13, 1 (2021), 1\u201325.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106511"},{"key":"e_1_3_2_48_2","first-page":"2069","volume-title":"Proceedings of the World Wide Web Conference","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In Proceedings of the World Wide Web Conference. 2069\u20132080."}],"container-title":["ACM Transactions on Spatial Algorithms and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641277","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3641277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:48Z","timestamp":1750295868000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":47,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3641277"],"URL":"https:\/\/doi.org\/10.1145\/3641277","relation":{},"ISSN":["2374-0353","2374-0361"],"issn-type":[{"value":"2374-0353","type":"print"},{"value":"2374-0361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"2023-05-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}