{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T02:21:54Z","timestamp":1777342914109,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2021QD014"],"award-info":[{"award-number":["No. 2021QD014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615204","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"4375-4379","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Geometry Interaction Augmented Graph Collaborative Filtering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3708-2823","authenticated-orcid":false,"given":"Jie","family":"Xu","sequence":"first","affiliation":[{"name":"Beijing Foreign Studies University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-1712","authenticated-orcid":false,"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Gregor Bachmann Gary B\u00e9cigneul and Octavian-Eugen Ganea. 2020. Constant Curvature Graph Convolutional Networks. In ICML.  Gregor Bachmann Gary B\u00e9cigneul and Octavian-Eugen Ganea. 2020. Constant Curvature Graph Convolutional Networks. In ICML."},{"key":"e_1_3_2_1_2_1","unstructured":"Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In NeurIPS. 4869--4880.  Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In NeurIPS. 4869--4880."},{"key":"e_1_3_2_1_3_1","volume-title":"Gao Cong, Lisi Chen, Jing Li, and Fan Li.","author":"Feng Shanshan","year":"2020","unstructured":"Shanshan Feng , Lucas Vinh Tran , Gao Cong, Lisi Chen, Jing Li, and Fan Li. 2020 . HME : A Hyperbolic Metric Embedding Approach for Next-POI Recommendation. In SIGIR. 1429--1438. Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li. 2020. HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation. In SIGIR. 1429--1438."},{"key":"e_1_3_2_1_4_1","volume-title":"Annales de l'institut Henri Poincar\u00e9","author":"Fr\u00e9chet Maurice","unstructured":"Maurice Fr\u00e9chet . 1948. Les \u00e9l\u00e9ments al\u00e9atoires de nature quelconque dans un espace distanci\u00e9 . In Annales de l'institut Henri Poincar\u00e9 , Vol. 10 . 215--310. Maurice Fr\u00e9chet. 1948. Les \u00e9l\u00e9ments al\u00e9atoires de nature quelconque dans un espace distanci\u00e9. In Annales de l'institut Henri Poincar\u00e9, Vol. 10. 215--310."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_6_1","unstructured":"Hermann Karcher. 1987. Riemannian comparison constructions. SFB 256.  Hermann Karcher. 1987. Riemannian comparison constructions. SFB 256."},{"key":"e_1_3_2_1_7_1","volume-title":"Riemannian center of mass and so called karcher mean. arXiv preprint arXiv:1407.2087","author":"Karcher Hermann","year":"2014","unstructured":"Hermann Karcher . 2014. Riemannian center of mass and so called karcher mean. arXiv preprint arXiv:1407.2087 ( 2014 ). Hermann Karcher. 2014. Riemannian center of mass and so called karcher mean. arXiv preprint arXiv:1407.2087 (2014)."},{"key":"e_1_3_2_1_8_1","volume-title":"Adam: A method for stochastic optimization. ICLR","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization. ICLR (2015). Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. ICLR (2015)."},{"key":"e_1_3_2_1_9_1","unstructured":"Marc Law Renjie Liao Jake Snell and Richard Zemel. 2019. Lorentzian Distance Learning for Hyperbolic Representations. In ICML. 3672--3681.  Marc Law Renjie Liao Jake Snell and Richard Zemel. 2019. Lorentzian Distance Learning for Hyperbolic Representations. In ICML. 3672--3681."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning. PMLR, 13209--13224","author":"Li Rui","year":"2022","unstructured":"Rui Li , Jianan Zhao , Chaozhuo Li , Di He , Yiqi Wang , Yuming Liu , Hao Sun , Senzhang Wang , Weiwei Deng , Yanming Shen , 2022 . House: Knowledge graph embedding with householder parameterization . In International Conference on Machine Learning. PMLR, 13209--13224 . Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, et al. 2022. House: Knowledge graph embedding with householder parameterization. In International Conference on Machine Learning. PMLR, 13209--13224."},{"key":"e_1_3_2_1_11_1","unstructured":"Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In NeurIPS. 8228--8239.  Qi Liu Maximilian Nickel and Douwe Kiela. 2019. Hyperbolic graph neural networks. In NeurIPS. 8228--8239."},{"key":"e_1_3_2_1_12_1","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In NeurIPS. 6338--6347.  Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In NeurIPS. 6338--6347."},{"key":"e_1_3_2_1_13_1","unstructured":"Maximilian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In ICML. 3779--3788.  Maximilian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In ICML. 3779--3788."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539128"},{"key":"e_1_3_2_1_15_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2012 . BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012). Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3592024"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Lucas Vinh Tran Yi Tay Shuai Zhang Gao Cong and Xiaoli Li. 2020. HyperML: a boosting metric learning approach in hyperbolic space for recommender systems. In WSDM. 609--617.  Lucas Vinh Tran Yi Tay Shuai Zhang Gao Cong and Xiaoli Li. 2020. HyperML: a boosting metric learning approach in hyperbolic space for recommender systems. In WSDM. 609--617.","DOI":"10.1145\/3336191.3371850"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.  Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3555372","article-title":"An adaptive graph pre-training framework for localized collaborative filtering","volume":"41","author":"Wang Yiqi","year":"2022","unstructured":"Yiqi Wang , Chaozhuo Li , Zheng Liu , Mingzheng Li , Jiliang Tang , Xing Xie , Lei Chen , and Philip S Yu . 2022 . An adaptive graph pre-training framework for localized collaborative filtering . ACM Transactions on Information Systems , Vol. 41 , 2 (2022), 1 -- 27 . Yiqi Wang, Chaozhuo Li, Zheng Liu, Mingzheng Li, Jiliang Tang, Xing Xie, Lei Chen, and Philip S Yu. 2022. An adaptive graph pre-training framework for localized collaborative filtering. ACM Transactions on Information Systems, Vol. 41, 2 (2022), 1--27.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_1_20_1","volume-title":"Continual Learning on Dynamic Graphs via Parameter Isolation. arXiv preprint arXiv:2305.13825","author":"Zhang Peiyan","year":"2023","unstructured":"Peiyan Zhang , Yuchen Yan , Chaozhuo Li , Senzhang Wang , Xing Xie , Guojie Song , and Sunghun Kim . 2023. Continual Learning on Dynamic Graphs via Parameter Isolation. arXiv preprint arXiv:2305.13825 ( 2023 ). Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, and Sunghun Kim. 2023. Continual Learning on Dynamic Graphs via Parameter Isolation. arXiv preprint arXiv:2305.13825 (2023)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531982"},{"key":"e_1_3_2_1_22_1","volume-title":"Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709","author":"Zhao Jianan","year":"2022","unstructured":"Jianan Zhao , Meng Qu , Chaozhuo Li , Hao Yan , Qian Liu , Rui Li , Xing Xie , and Jian Tang . 2022. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709 ( 2022 ). Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. 2022. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709 (2022)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591642"},{"key":"e_1_3_2_1_24_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Zhu Shichao","year":"2020","unstructured":"Shichao Zhu , Shirui Pan , Chuan Zhou , Jia Wu , Yanan Cao , and Bin Wang . 2020 . Graph Geometry Interaction Learning . Advances in Neural Information Processing Systems , Vol. 33 (2020). Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. 2020. Graph Geometry Interaction Learning. Advances in Neural Information Processing Systems, Vol. 33 (2020)."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615204","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615204","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:57Z","timestamp":1750178217000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":24,"alternative-id":["10.1145\/3583780.3615204","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615204","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}