{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T03:46:45Z","timestamp":1781927205574,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["U20B2045"],"award-info":[{"award-number":["U20B2045"]}]},{"name":"the National Natural Science Foundation of China","award":["62192784"],"award-info":[{"award-number":["62192784"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3531982","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:13Z","timestamp":1657206733000},"page":"80-90","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":56,"title":["Geometric Disentangled Collaborative Filtering"],"prefix":"10.1145","author":[{"given":"Yiding","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Deng","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","author":"Adomavicius Gediminas","year":"2005","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin . 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions . IEEE transactions on knowledge and data engineering, Vol. 17 , 6 ( 2005 ), 734--749. Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, Vol. 17, 6 (2005), 734--749."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_2_3_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_2_4_1","unstructured":"Gary Becigneul and Octavian-Eugen Ganea. 2019. Riemannian Adaptive Optimization Methods. In ICLR.  Gary Becigneul and Octavian-Eugen Ganea. 2019. Riemannian Adaptive Optimization Methods. In ICLR."},{"key":"e_1_3_2_2_5_1","volume-title":"A panoramic view of Riemannian geometry","author":"Berger Marcel","unstructured":"Marcel Berger . 2012. A panoramic view of Riemannian geometry . Springer Science & Business Media . Marcel Berger. 2012. A panoramic view of Riemannian geometry. Springer Science & Business Media."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2039320"},{"key":"e_1_3_2_2_7_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_2_8_1","volume-title":"NeurIPS","volume":"34","author":"Chen Hong","year":"2021","unstructured":"Hong Chen , Yudong Chen , Xin Wang , Ruobing Xie , Rui Wang , Feng Xia , and Wenwu Zhu . 2021 . Curriculum Disentangled Recommendation with Noisy Multi-feedback . NeurIPS , Vol. 34 (2021). Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021. Curriculum Disentangled Recommendation with Noisy Multi-feedback. NeurIPS, Vol. 34 (2021)."},{"key":"e_1_3_2_2_9_1","volume-title":"Thomas Kipf, and Jakub M Tomczak.","author":"Davidson Tim R","year":"2018","unstructured":"Tim R Davidson , Luca Falorsi , Nicola De Cao , Thomas Kipf, and Jakub M Tomczak. 2018 . Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018). Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. 2018. Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018)."},{"key":"e_1_3_2_2_10_1","unstructured":"Octavian Ganea Gary B\u00e9cigneul and Thomas Hofmann. 2018. Hyperbolic neural networks. In NeurIPS. 5350--5360.  Octavian Ganea Gary B\u00e9cigneul and Thomas Hofmann. 2018. Hyperbolic neural networks. In NeurIPS. 5350--5360."},{"key":"e_1_3_2_2_11_1","unstructured":"Albert Gu Frederic Sala Beliz Gunel and Christopher R\u00e9. 2018. Learning mixed-curvature representations in product spaces. In ICLR.  Albert Gu Frederic Sala Beliz Gunel and Christopher R\u00e9. 2018. Learning mixed-curvature representations in product spaces. In ICLR."},{"key":"e_1_3_2_2_12_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis)","author":"Maxwell Harper F","year":"2015","unstructured":"F Maxwell Harper and Joseph A Konstan . 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) , Vol. 5 , 4 ( 2015 ), 1--19. F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"S\u00f8ren Hauberg. 2018. Directional statistics with the spherical normal distribution. In FUSION. 704--711.  S\u00f8ren Hauberg. 2018. Directional statistics with the spherical normal distribution. In FUSION. 704--711.","DOI":"10.23919\/ICIF.2018.8455242"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_16_1","volume-title":"Lie groups, and symmetric spaces.","author":"Helgason Sigurdur","unstructured":"Sigurdur Helgason . 1979. Differential geometry , Lie groups, and symmetric spaces. Vol. 80 . Academic press . Sigurdur Helgason. 1979. Differential geometry, Lie groups, and symmetric spaces. Vol. 80. Academic press."},{"key":"e_1_3_2_2_17_1","unstructured":"Irina Higgins Loic Matthey Arka Pal Christopher Burgess Xavier Glorot Matthew Botvinick Shakir Mohamed and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In ICLR. 1024--1034.  Irina Higgins Loic Matthey Arka Pal Christopher Burgess Xavier Glorot Matthew Botvinick Shakir Mohamed and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In ICLR. 1024--1034."},{"key":"e_1_3_2_2_18_1","unstructured":"Jun-Ting Hsieh Bingbin Liu De-An Huang Li Fei-Fei and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In NeurIPS. 515--524.  Jun-Ting Hsieh Bingbin Liu De-An Huang Li Fei-Fei and Juan Carlos Niebles. 2018. Learning to decompose and disentangle representations for video prediction. In NeurIPS. 515--524."},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of 2nd Berkeley Symposium","author":"Kuhn","year":"1951","unstructured":"Kuhn HW and T AW. 1951 . Nonlinear programming . In Proceedings of 2nd Berkeley Symposium , Berkeley: University of California Press. 481--492. Kuhn HW and T AW. 1951. Nonlinear programming. In Proceedings of 2nd Berkeley Symposium, Berkeley: University of California Press. 481--492."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Vineet John Lili Mou Hareesh Bahuleyan and Olga Vechtomova. 2019. Disentangled Representation Learning for Non-Parallel Text Style Transfer. In ACL. 424--434.  Vineet John Lili Mou Hareesh Bahuleyan and Olga Vechtomova. 2019. Disentangled Representation Learning for Non-Parallel Text Style Transfer. In ACL. 424--434.","DOI":"10.18653\/v1\/P19-1041"},{"key":"e_1_3_2_2_21_1","volume-title":"Minima of Functions of Several Variables with Inequalities as Side Conditions. Master's thesis. Department of Mathematics","author":"Karush William","unstructured":"William Karush . 1939. Minima of Functions of Several Variables with Inequalities as Side Conditions. Master's thesis. Department of Mathematics , University of Chicago , Chicago, IL, USA . William Karush. 1939. Minima of Functions of Several Variables with Inequalities as Side Conditions. Master's thesis. Department of Mathematics, University of Chicago, Chicago, IL, USA."},{"key":"e_1_3_2_2_22_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_2_23_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"e_1_3_2_2_26_1","unstructured":"Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In SIGKDD. 305--314.  Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In SIGKDD. 305--314."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Dawen Liang Rahul G Krishnan Matthew D Hoffman and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.  Dawen Liang Rahul G Krishnan Matthew D Hoffman and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01200757"},{"key":"e_1_3_2_2_29_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_2_30_1","unstructured":"Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019 a. Disentangled graph convolutional networks. In ICML. PMLR 4212--4221.  Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019 a. Disentangled graph convolutional networks. In ICML. PMLR 4212--4221."},{"key":"e_1_3_2_2_31_1","unstructured":"Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019 b. Learning disentangled representations for recommendation. In NeurIPS. 5711--5722.  Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019 b. Learning disentangled representations for recommendation. In NeurIPS. 5711--5722."},{"key":"e_1_3_2_2_32_1","unstructured":"Jianxin Ma Chang Zhou Hongxia Yang Peng Cui Xin Wang and Wenwu Zhu. 2020 a. Disentangled Self-Supervision in Sequential Recommenders. In SIGKDD. 483--491.  Jianxin Ma Chang Zhou Hongxia Yang Peng Cui Xin Wang and Wenwu Zhu. 2020 a. Disentangled Self-Supervision in Sequential Recommenders. In SIGKDD. 483--491."},{"key":"e_1_3_2_2_33_1","unstructured":"Jianxin Ma Chang Zhou Hongxia Yang Peng Cui Xin Wang and Wenwu Zhu. 2020 b. Disentangled self-supervision in sequential recommenders. In SIGKDD. 483--491.  Jianxin Ma Chang Zhou Hongxia Yang Peng Cui Xin Wang and Wenwu Zhu. 2020 b. Disentangled self-supervision in sequential recommenders. In SIGKDD. 483--491."},{"key":"e_1_3_2_2_34_1","volume-title":"Chris J Maddison, Ryota Tomioka, and Yee Whye Teh.","author":"Mathieu Emile","year":"2019","unstructured":"Emile Mathieu , Charline Le Lan , Chris J Maddison, Ryota Tomioka, and Yee Whye Teh. 2019 . Continuous Hierarchical Representations with Poincar\u00e9 Variational Auto-Encoders . arXiv preprint arXiv:1901.06033 (2019). Emile Mathieu, Charline Le Lan, Chris J Maddison, Ryota Tomioka, and Yee Whye Teh. 2019. Continuous Hierarchical Representations with Poincar\u00e9 Variational Auto-Encoders. arXiv preprint arXiv:1901.06033 (2019)."},{"key":"e_1_3_2_2_35_1","unstructured":"Emile Mathieu and Maximilian Nickel. 2020. Riemannian Continuous Normalizing Flows. In NeurIPS. 2503--2515.  Emile Mathieu and Maximilian Nickel. 2020. Riemannian Continuous Normalizing Flows. In NeurIPS. 2503--2515."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Leyla Mirvakhabova Evgeny Frolov Valentin Khrulkov Ivan Oseledets and Alexander Tuzhilin. 2020. Performance of hyperbolic geometry models on top-N recommendation tasks. In RecSys. 527--532.  Leyla Mirvakhabova Evgeny Frolov Valentin Khrulkov Ivan Oseledets and Alexander Tuzhilin. 2020. Performance of hyperbolic geometry models on top-N recommendation tasks. In RecSys. 527--532.","DOI":"10.1145\/3383313.3412219"},{"key":"e_1_3_2_2_37_1","unstructured":"Yoshihiro Nagano Shoichiro Yamaguchi Yasuhiro Fujita and Masanori Koyama. 2019. A wrapped normal distribution on hyperbolic space for gradient-based learning. In ICML. 4693--4702.  Yoshihiro Nagano Shoichiro Yamaguchi Yasuhiro Fujita and Masanori Koyama. 2019. A wrapped normal distribution on hyperbolic space for gradient-based learning. In ICML. 4693--4702."},{"key":"e_1_3_2_2_38_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_2_39_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_2_40_1","volume-title":"Hyperbolic deep neural networks: A survey. arXiv preprint arXiv:2101.04562","author":"Peng Wei","year":"2021","unstructured":"Wei Peng , Tuomas Varanka , Abdelrahman Mostafa , Henglin Shi , and Guoying Zhao . 2021. Hyperbolic deep neural networks: A survey. arXiv preprint arXiv:2101.04562 ( 2021 ). Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, and Guoying Zhao. 2021. Hyperbolic deep neural networks: A survey. arXiv preprint arXiv:2101.04562 (2021)."},{"key":"e_1_3_2_2_41_1","volume-title":"Albert Gu, and Christopher R\u00e9.","author":"Sala Frederic","year":"2018","unstructured":"Frederic Sala , Chris De Sa , Albert Gu, and Christopher R\u00e9. 2018 . Representation tradeoffs for hyperbolic embeddings. In ICML. 4457--4466. Frederic Sala, Chris De Sa, Albert Gu, and Christopher R\u00e9. 2018. Representation tradeoffs for hyperbolic embeddings. In ICML. 4457--4466."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Aravind Sankar Yozen Liu Jun Yu and Neil Shah. 2021. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. In WWW. 2535--2546.  Aravind Sankar Yozen Liu Jun Yu and Neil Shah. 2021. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. In WWW. 2535--2546.","DOI":"10.1145\/3442381.3450120"},{"key":"e_1_3_2_2_43_1","unstructured":"Ondrej Skopek Octavian-Eugen Ganea and Gary B\u00e9cigneul. 2019. Mixed-curvature Variational Autoencoders. In ICLR.  Ondrej Skopek Octavian-Eugen Ganea and Gary B\u00e9cigneul. 2019. Mixed-curvature Variational Autoencoders. In ICLR."},{"key":"e_1_3_2_2_44_1","volume-title":"A survey of collaborative filtering techniques. Advances in artificial intelligence","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M Khoshgoftaar . 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence , Vol. 2009 ( 2009 ). Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009)."},{"key":"e_1_3_2_2_45_1","volume-title":"HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601.","author":"Sun Jianing","year":"2021","unstructured":"Jianing Sun , Zhaoyue Cheng , Saba Zuberi , Felipe P\u00e9rez , and Maksims Volkovs . 2021 . HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601. Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe P\u00e9rez, and Maksims Volkovs. 2021. HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601."},{"key":"e_1_3_2_2_46_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_2_47_1","doi-asserted-by":"crossref","unstructured":"Hao Wang Naiyan Wang and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In SIGKDD. 1235--1244.  Hao Wang Naiyan Wang and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In SIGKDD. 1235--1244.","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_3_2_2_48_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_2_49_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Hongye Jin An Zhang Xiangnan He Tong Xu and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010.  Xiang Wang Hongye Jin An Zhang Xiangnan He Tong Xu and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010.","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2316836"},{"key":"e_1_3_2_2_51_1","volume-title":"NeurIPS","volume":"33","author":"Yang Yiding","year":"2020","unstructured":"Yiding Yang , Zunlei Feng , Mingli Song , and Xinchao Wang . 2020 . Factorizable Graph Convolutional Networks . NeurIPS , Vol. 33 (2020). Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang. 2020. Factorizable Graph Convolutional Networks. NeurIPS, Vol. 33 (2020)."},{"key":"e_1_3_2_2_52_1","volume-title":"2021 a. Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems. arXiv preprint arXiv:2105.08908","author":"Zhang Sixiao","year":"2021","unstructured":"Sixiao Zhang , Hongxu Chen , Xiao Ming , Lizhen Cui , Hongzhi Yin , and Guandong Xu . 2021 a. Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems. arXiv preprint arXiv:2105.08908 ( 2021 ). Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, and Guandong Xu. 2021 a. Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems. arXiv preprint arXiv:2105.08908 (2021)."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Yiding Zhang Xiao Wang Chuan Shi Nian Liu and Guojie Song. 2021 b. Lorentzian Graph Convolutional Networks. In WWW. 1249--1261.  Yiding Zhang Xiao Wang Chuan Shi Nian Liu and Guojie Song. 2021 b. Lorentzian Graph Convolutional Networks. In WWW. 1249--1261.","DOI":"10.1145\/3442381.3449872"},{"key":"e_1_3_2_2_54_1","volume-title":"Disentangling User Interest and Conformity for Recommendation with Causal Embedding. arXiv preprint arXiv:2006.11011","author":"Zheng Yu","year":"2020","unstructured":"Yu Zheng , Chen Gao , Xiang Li , Xiangnan He , Depeng Jin , and Yong Li. 2020. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. arXiv preprint arXiv:2006.11011 ( 2020 ). Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, and Yong Li. 2020. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. arXiv preprint arXiv:2006.11011 (2020)."}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531982","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3531982","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:20Z","timestamp":1750183820000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531982"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":54,"alternative-id":["10.1145\/3477495.3531982","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3531982","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}