{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:11:58Z","timestamp":1784178718320,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3412754","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T05:31:06Z","timestamp":1603085466000},"page":"2861-2868","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["GraphSAIL"],"prefix":"10.1145","author":[{"given":"Yishi","family":"Xu","sequence":"first","affiliation":[{"name":"Mila, Universit\u00e9 de Montr\u00e9al, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab Montreal Research Center, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Coates","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Francisco M. Castro Manuel J. Mar\u00edn-Jim\u00e9nez Nicol\u00e1s Guil Cordelia Schmid and Karteek Alahari. 2018. End-to-End Incremental Learning.  Francisco M. Castro Manuel J. Mar\u00edn-Jim\u00e9nez Nicol\u00e1s Guil Cordelia Schmid and Karteek Alahari. 2018. End-to-End Incremental Learning.","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330964"},{"key":"e_1_3_2_2_3_1","volume-title":"Wide & Deep Learning for Recommender Systems. In The ACM Recommender Systems conference. DLRS workshop.","author":"Cheng Heng-Tze","year":"2016","unstructured":"Heng-Tze Cheng , Levent Koc , Jeremiah Harmsen , Tal Shaked , Tushar Chandra , 2016 . Wide & Deep Learning for Recommender Systems. In The ACM Recommender Systems conference. DLRS workshop. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, et almbox. 2016. Wide & Deep Learning for Recommender Systems. In The ACM Recommender Systems conference. DLRS workshop."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_2_5_1","volume-title":"Proc. Int. Conf. Machine Learning (ICML).","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017 . Neural message passing for quantum chemistry . In Proc. Int. Conf. Machine Learning (ICML). Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In Proc. Int. Conf. Machine Learning (ICML)."},{"key":"e_1_3_2_2_6_1","volume-title":"Proc. Int. Joint Conf. Artificial Intelligence.","author":"Gori Marco","year":"2007","unstructured":"Marco Gori and Augusto Pucci . 2007 . ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines . In Proc. Int. Joint Conf. Artificial Intelligence. Marco Gori and Augusto Pucci. 2007. ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. In Proc. Int. Joint Conf. Artificial Intelligence."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_2_2_8_1","volume-title":"Proc. Adv. Neural Inf. Proc. Systems.","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton , Zhitao Ying , and Jure Leskovec . 2017 . Inductive Representation Learning on Large Graphs . In Proc. Adv. Neural Inf. Proc. Systems. William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Proc. Adv. Neural Inf. Proc. Systems."},{"key":"e_1_3_2_2_9_1","volume-title":"Distilling the knowledge in a neural network. arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2015. Distilling the knowledge in a neural network. arXiv:1503.02531 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_2_10_1","volume-title":"Proc. Int. Conf. Learning Representations.","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks . In Proc. Int. Conf. Learning Representations. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proc. Int. Conf. Learning Representations."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331283"},{"key":"e_1_3_2_2_13_1","volume-title":"2019 b. Sparse online collaborative filtering with dynamic regularization. Information Sciences","author":"Li Kangkang","year":"2019","unstructured":"Kangkang Li , Xiuze Zhou , Fan Lin , Wenhua Zeng , Beizhan Wang , and Gil Alterovitz . 2019 b. Sparse online collaborative filtering with dynamic regularization. Information Sciences ( 2019 ). Kangkang Li, Xiuze Zhou, Fan Lin, Wenhua Zeng, Beizhan Wang, and Gil Alterovitz. 2019 b. Sparse online collaborative filtering with dynamic regularization. Information Sciences (2019)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401109"},{"key":"e_1_3_2_2_17_1","unstructured":"Yanru Qu Bohui Fang Weinan Zhang Ruiming Tang Minzhe Niu Huifeng Guo Yong Yu and Xiuqiang He. 2019. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data. ACM Trans. Inf. System. (2019).  Yanru Qu Bohui Fang Weinan Zhang Ruiming Tang Minzhe Niu Huifeng Guo Yong Yu and Xiuqiang He. 2019. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data. ACM Trans. Inf. System. (2019)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_3_2_2_19_1","volume-title":"Proc. Conf. Uncertainty in Artificial Intelligence.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian Personalized Ranking from Implicit Feedback . In Proc. Conf. Uncertainty in Artificial Intelligence. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proc. Conf. Uncertainty in Artificial Intelligence."},{"key":"e_1_3_2_2_20_1","volume-title":"Incremental Learning of Object Detectors Without Catastrophic Forgetting. In Int. Conf. on Computer Vision (ICCV).","author":"Shmelkov Konstantin","year":"2017","unstructured":"Konstantin Shmelkov , Cordelia Schmid , and Karteek Alahari . 2017 . Incremental Learning of Object Detectors Without Catastrophic Forgetting. In Int. Conf. on Computer Vision (ICCV). Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari. 2017. Incremental Learning of Object Detectors Without Catastrophic Forgetting. In Int. Conf. on Computer Vision (ICCV)."},{"key":"e_1_3_2_2_21_1","volume-title":"Proc. Int. Conf. on Learning Representations.","author":"Srinivasan Balasubramaniam","year":"2020","unstructured":"Balasubramaniam Srinivasan and Bruno Ribeiro . 2020 . On the Equivalence between Positional Node Embeddings and Structural Graph Representations . In Proc. Int. Conf. on Learning Representations. Balasubramaniam Srinivasan and Bruno Ribeiro. 2020. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. In Proc. Int. Conf. on Learning Representations."},{"key":"e_1_3_2_2_22_1","volume-title":"Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining (ICDM).","author":"Sun Jianing","year":"2019","unstructured":"Jianing Sun , Yingxue Zhang , Chen Ma , Mark Coates , Huifeng Guo , Ruiming Tang , and Xiuqiang He . 2019 . Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining (ICDM). Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, and Xiuqiang He. 2019. Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining (ICDM)."},{"key":"e_1_3_2_2_23_1","volume-title":"Proc. ACM Conf. Knowledge Discovery and Data Mining (DLD).","author":"van den Berg Rianne","year":"2018","unstructured":"Rianne van den Berg , Thomas N Kipf , and Max Welling . 2018 . Graph Convolutional Matrix Completion . In Proc. ACM Conf. Knowledge Discovery and Data Mining (DLD). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2018. Graph Convolutional Matrix Completion. In Proc. ACM Conf. Knowledge Discovery and Data Mining (DLD)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_25_1","unstructured":"Ju Xu and Zhanxing Zhu. 2018. Reinforced continual learning. In Advances in Neural Information Processing Systems.  Ju Xu and Zhanxing Zhu. 2018. Reinforced continual learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330865"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_30_1","volume-title":"Proc. Int. Conf. Machine Learning.","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You , Rex Ying , and Jure Leskovec . 2019 . Position-aware graph neural networks . In Proc. Int. Conf. Machine Learning. Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In Proc. Int. Conf. Machine Learning."},{"key":"e_1_3_2_2_31_1","volume-title":"Incremental learning for matrix factorization in recommender systems","author":"Yu Tong","unstructured":"Tong Yu , Ole J Mengshoel , Alvin Jude , Eugen Feller , Julien Forgeat , and Nimish Radia . 2016. Incremental learning for matrix factorization in recommender systems . In IEEE Int.l Conf. on Big Data . Tong Yu, Ole J Mengshoel, Alvin Jude, Eugen Feller, Julien Forgeat, and Nimish Radia. 2016. Incremental learning for matrix factorization in recommender systems. In IEEE Int.l Conf. on Big Data."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015829"},{"key":"e_1_3_2_2_33_1","volume-title":"Continual Graph Learning. arXiv:2003.09908","author":"Zhou Fan","year":"2020","unstructured":"Fan Zhou , Chengtai Cao , Ting Zhong , Kunpeng Zhang , Goce Trajcevski , and Ji Geng . 2020. Continual Graph Learning. arXiv:2003.09908 ( 2020 ). Fan Zhou, Chengtai Cao, Ting Zhong, Kunpeng Zhang, Goce Trajcevski, and Ji Geng. 2020. Continual Graph Learning. arXiv:2003.09908 (2020)."}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland","acronym":"CIKM '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412754","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3412754","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:55Z","timestamp":1750197775000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412754"}},"subtitle":["Graph Structure Aware Incremental Learning for Recommender Systems"],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":33,"alternative-id":["10.1145\/3340531.3412754","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3412754","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}