{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T06:09:35Z","timestamp":1782713375049,"version":"3.54.5"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"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":["62036005"],"award-info":[{"award-number":["62036005"]}],"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. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>POI recommendation plays an important role in many applications, such as mobility prediction and location-based advertisements. Existing POI recommendation methods mainly capture the observed patterns in user visits for recommendations, without a comprehensive consideration of the underlying reasons behind the visits. Therefore, different causes of a visit, i.e., users\u2019 interest and geographical context, are entangled. When the underlying causes change (e.g., when a user moves to a new place), the robustness of the recommendations cannot be guaranteed. To address the above challenges, we propose DUIG, a novel user interest and geographical influences disentanglement framework for POI recommendations. We first design a personalized disentanglement strategy to divide check-ins through geographical influence. Specifically, the colliding effect of causality is leveraged to the divide cause-specific check-ins, such that user interest and geographical influence can be properly disentangled in user and POI embeddings. Through this mechanism, even if the underlying reasons that affect a user\u2019s preference change, intervention can be conducted upon the causes to make recommendations generalized to the new scenario. In addition, a geographical-aware negative sampling strategy is proposed to utilize hard negatives to regularize the embedding and disentanglement in the latent space, where a larger sampling probability is introduced for negative samples containing more geographic information. Extensive experiments on two real-world POI recommendation datasets demonstrate the superior performance of DUIG.<\/jats:p>","DOI":"10.1145\/3723008","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T14:01:21Z","timestamp":1741701681000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Disentangling User Interest and Geographical Context for POI Recommendations"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3466-4496","authenticated-orcid":false,"given":"Wenhui","family":"Meng","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-326X","authenticated-orcid":false,"given":"Jiayi","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-1500","authenticated-orcid":false,"given":"Jing","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-2788","authenticated-orcid":false,"given":"Yaochen","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-1066","authenticated-orcid":false,"given":"Zhenzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"385","volume-title":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W. 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In Proceedings of the 32nd Advances in Neural Information Processing Systems, 5712\u20135723."},{"key":"e_1_3_2_29_2","first-page":"429","volume-title":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Morik Marco","year":"2020","unstructured":"Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 429\u2013438."},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Causality. 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In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 309\u2013318."},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1145\/3383313.3412262","volume-title":"Proceedings of the ACM Conference on Recommender Systems","author":"Saito Yuta","year":"2020","unstructured":"Yuta Saito. 2020. Doubly robust estimator for ranking metrics with post-click conversions. In Proceedings of the ACM Conference on Recommender Systems, 92\u2013100."},{"key":"e_1_3_2_38_2","first-page":"5","volume-title":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Saito Yuta","year":"2020","unstructured":"Yuta Saito. 2020. Unbiased pairwise learning from biased implicit feedback. 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Bridging collaborative filtering and semi-supervised learning: A neural approach for poi recommendation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1245\u20131254."},{"key":"e_1_3_2_55_2","first-page":"1144","volume-title":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yang Song","year":"2022","unstructured":"Song Yang, Jiamou Liu, and Kaiqi Zhao. 2022. GETNext: Trajectory flow map enhanced transformer for next POI recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 1144\u20131153."},{"key":"e_1_3_2_56_2","first-page":"458","volume-title":"Proceedings of the International Conference on Advances in Geographic Information Systems","author":"Ye Mao","year":"2010","unstructured":"Mao Ye, Peifeng Yin, and Wang-Chien Lee. 2010. Location recommendation for location-based social networks. 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In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 443\u2013452."},{"issue":"3","key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s11280-018-0579-9","article-title":"Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation","volume":"22","author":"Zhang Zhiyuan","year":"2019","unstructured":"Zhiyuan Zhang, Yun Liu, Zhenjiang Zhang, and Bo Shen. 2019. Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation. 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In Proceedings of the IEEE International Conference on Data Engineering, 675\u2013686."},{"key":"e_1_3_2_62_2","first-page":"153","volume-title":"Proceedings of the International Conference on World Wide Web","author":"Zhao Shenglin","year":"2017","unstructured":"Shenglin Zhao, Tong Zhao, Irwin King, and Michael R. Lyu. 2017. Geo-Teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In Proceedings of the International Conference on World Wide Web, 153\u2013162."},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the 12th International Conference on World Wide Web","author":"Zheng Yu","year":"2012","unstructured":"Yu Zheng. 2012. Tutorial on location-based social networks. In Proceedings of the 12th International Conference on World Wide Web."},{"key":"e_1_3_2_64_2","first-page":"2980","volume-title":"Proceedings of the International Conference on World Wide Web","author":"Zheng Yu","year":"2021","unstructured":"Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the International Conference on World Wide Web, 2980\u20132991."},{"key":"e_1_3_2_65_2","first-page":"207","volume-title":"Proceedings of the Machine Learning for Causal Inference","author":"Zhu Yaochen","year":"2023","unstructured":"Yaochen Zhu, Jing Ma, and Jundong Li. 2023. Causal inference and recommendations. In Proceedings of the Machine Learning for Causal Inference, 207\u2013245."},{"key":"e_1_3_2_66_2","first-page":"3638","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zhu Yaochen","year":"2023","unstructured":"Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. 2023. Path-specific counterfactual fairness for recommender systems. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3638\u20133649."},{"issue":"4","key":"e_1_3_2_67_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3653985","article-title":"Deep causal reasoning for recommendations","volume":"15","author":"Zhu Yaochen","year":"2024","unstructured":"Yaochen Zhu, Jing Yi, Jiayi Xie, and Zhenzhong Chen. 2024. Deep causal reasoning for recommendations. 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