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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this article, we present a GRM-RTrip (short for\n            <jats:bold>G<\/jats:bold>\n            raph-based\n            <jats:bold>R<\/jats:bold>\n            epresentation\n            <jats:bold>M<\/jats:bold>\n            ethod for\n            <jats:bold>R<\/jats:bold>\n            einforce\n            <jats:bold>Trip<\/jats:bold>\n            Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.\n          <\/jats:p>","DOI":"10.1145\/3564609","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T11:22:52Z","timestamp":1664277772000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Trip Reinforcement Recommendation with Graph-based Representation Learning"],"prefix":"10.1145","volume":"17","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\/0000-0002-1286-2578","authenticated-orcid":false,"given":"Haicheng","family":"Tao","sequence":"additional","affiliation":[{"name":"Nanjing University of Finance and Economics, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-021-01567-3"},{"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":"10.1016\/j.eswa.2019.113070"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372278.3390727"},{"key":"e_1_3_2_6_2","first-page":"1052","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Xinshi","year":"2019","unstructured":"Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2019. Generative adversarial user model for reinforcement learning based recommendation system. In Proceedings of the International Conference on Machine Learning. 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Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Transactions on Information Systems 35, 1 (2016), 5.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_40_2","article-title":"Personalized graph neural networks with attention mechanism for session-aware recommendation","author":"Zhang Mengqi","year":"2020","unstructured":"Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, and Liang Wang. 2020. Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3946\u20133957.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0079"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3091615"},{"key":"e_1_3_2_43_2","article-title":"Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation","author":"Zhao Pengpeng","year":"2019","unstructured":"Pengpeng Zhao, Chengfeng Xu, Yanchi Liu, Victor S. Sheng, Kai Zheng, Hui Xiong, and Xiaofang Zhou. 2019. Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation. 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