{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:36:25Z","timestamp":1779316585920,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Institute of Information & com- munications Technology Planning & evaluation (IITP) funded by the Korea government (MSIT)","award":["2019-0-00079"],"award-info":[{"award-number":["2019-0-00079"]}]},{"name":"the National Research Foundation of Korea (NRF)","award":["2021R1C1C1005407"],"award-info":[{"award-number":["2021R1C1C1005407"]}]},{"name":"the Institute of Information & com- munications Technology Planning & evaluation (IITP) funded by the Korea government (MSIT)","award":["2019-0-00421"],"award-info":[{"award-number":["2019-0-00421"]}]},{"name":"the National Research Foundation of Korea (NRF)","award":["2022R1F1A1074334"],"award-info":[{"award-number":["2022R1F1A1074334"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583241","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"1478-1488","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-2391","authenticated-orcid":false,"given":"Heesoo","family":"Jung","sequence":"first","affiliation":[{"name":"Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7349-0018","authenticated-orcid":false,"given":"Sangpil","family":"Kim","sequence":"additional","affiliation":[{"name":"Dept. of Artificial Intelligence, Korea University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-5806","authenticated-orcid":false,"given":"Hogun","family":"Park","sequence":"additional","affiliation":[{"name":"Dept. of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea and Dept. of Artificial Intelligence, Sungkyunkwan University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proc. of UAI. 841\u2013851","author":"Abu-El-Haija Sami","year":"2020","unstructured":"Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, and Joonseok Lee. 2020. N-gcn: Multi-scale graph convolution for semi-supervised node classification. In Proc. of UAI. 841\u2013851."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Smriti Bhagat Graham Cormode and S Muthukrishnan. 2011. Node classification in social networks. In Social network data analytics. 115\u2013148.","DOI":"10.1007\/978-1-4419-8462-3_5"},{"key":"e_1_3_2_2_3_1","volume-title":"Proc. of ICML.","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 Proc. of ICML."},{"key":"e_1_3_2_2_4_1","volume-title":"Proc. of NeurIPS. 1025\u20131035","author":"Hamilton L","year":"2017","unstructured":"William\u00a0L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proc. of NeurIPS. 1025\u20131035."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219965"},{"key":"e_1_3_2_2_9_1","volume-title":"Proc. of ICLR","author":"Kipf N","year":"2017","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. Proc. of ICLR (2017)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403088"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401237"},{"key":"e_1_3_2_2_13_1","volume-title":"Proc. of ICML. 1928\u20131937","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih, Adria\u00a0Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proc. of ICML. 1928\u20131937."},{"key":"e_1_3_2_2_14_1","volume-title":"Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602","author":"Mnih Volodymyr","year":"2013","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)."},{"key":"e_1_3_2_2_15_1","volume-title":"Human-level control through deep reinforcement learning. Nature 518, 7540","author":"Mnih Volodymyr","year":"2015","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei\u00a0A Rusu, Joel Veness, Marc\u00a0G Bellemare, Alex Graves, Martin Riedmiller, Andreas\u00a0K Fidjeland, Georg Ostrovski, 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529\u2013533."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531934"},{"key":"e_1_3_2_2_17_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)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010002"},{"key":"e_1_3_2_2_19_1","volume-title":"An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747","author":"Ruder Sebastian","year":"2016","unstructured":"Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)."},{"key":"e_1_3_2_2_20_1","volume-title":"Proc. of NeurIPS. 1057\u20131063","author":"Sutton S","year":"2000","unstructured":"Richard\u00a0S Sutton, David\u00a0A McAllester, Satinder\u00a0P Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Proc. of NeurIPS. 1057\u20131063."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6073"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450133"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380098"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401141"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331203"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467451"},{"key":"e_1_3_2_2_31_1","volume-title":"Proc. of ICML. 5453\u20135462","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In Proc. of ICML. 5453\u20135462."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983385"},{"key":"e_1_3_2_2_33_1","volume-title":"Learning over knowledge-base embeddings for recommendation. arXiv preprint arXiv:1803.06540","author":"Zhang Yongfeng","year":"2018","unstructured":"Yongfeng Zhang, Qingyao Ai, Xu Chen, and Pengfei Wang. 2018. Learning over knowledge-base embeddings for recommendation. arXiv preprint arXiv:1803.06540 (2018)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401181"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583241","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583241","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:19Z","timestamp":1750183759000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":34,"alternative-id":["10.1145\/3543507.3583241","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583241","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}