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Asghar, \u201cYelp dataset challenge: Review rating prediction,\u201d arXiv preprint arXiv:1605.05362, 2016."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] G. Linden, B. Smith, and J. York, \u201cAmazon.com recommendations: Item-to-item collaborative filtering,\u201d IEEE Internet Computing, vol.7, no.1, pp.76-80, 2003. 10.1109\/mic.2003.1167344","DOI":"10.1109\/MIC.2003.1167344"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Y. Shen, Y. Wu, Y. Zhang, C. Shan, J. Zhang, B.K. Letaief, and D. Li, \u201cHow powerful is graph convolution for recommendation?,\u201d Proc. 30th ACM Int. Conf. information & knowledge management, pp.1619-1629, 2021. 10.1145\/3459637.3482264","DOI":"10.1145\/3459637.3482264"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] C. Wang, Y. Yu, W. Ma, M. Zhang, C. Chen, Y. Liu, and S. Ma, \u201cTowards representation alignment and uniformity in collaborative filtering,\u201d Proc. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.1816-1825, 2022. 10.1145\/3534678.3539253","DOI":"10.1145\/3534678.3539253"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie, \u201cSelf-supervised graph learning for recommendation,\u201d Proc. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.726-735, 2021. 10.1145\/3404835.3462862","DOI":"10.1145\/3404835.3462862"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, \u201cUsing collaborative filtering to weave an information tapestry,\u201d Communications of the ACM, vol.35, no.12, pp.61-70, 1992. 10.1145\/138859.138867","DOI":"10.1145\/138859.138867"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Y. Koren, R. Bell, and C. Volinsky, \u201cMatrix factorization techniques for recommender systems,\u201d Computer, vol.42, no.8, pp.30-37, 2009. 10.1109\/mc.2009.263","DOI":"10.1109\/MC.2009.263"},{"key":"11","unstructured":"[11] M. Defferrard, X. Bresson, and P. Vandergheynst, \u201cConvolutional neural networks on graphs with fast localized spectral filtering,\u201d Advances in Neural Information Processing Systems, vol.29, 2016."},{"key":"12","unstructured":"[12] R.v.d. Berg, T.N. Kipf, and M. Welling, \u201cGraph convolutional matrix completion,\u201d arXiv preprint arXiv:1706.02263, 2017."},{"key":"13","unstructured":"[13] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, \u201cBpr: Bayesian personalized ranking from implicit feedback,\u201d Proc. Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, p.452-461, 2009."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Z. Gao, Z. Cheng, F. P\u00e9rez, J. Sun, and M. Volkovs, \u201cMcl: Mixed-centric loss for collaborative filtering,\u201d Proc. ACM Web Conference 2022, pp.2339-2347, 2022. 10.1145\/3485447.3512106","DOI":"10.1145\/3485447.3512106"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] K. Mao, J. Zhu, J. Wang, Q. Dai, Z. Dong, X. Xiao, and X. He, \u201cSimplex: A simple and strong baseline for collaborative filtering,\u201d Proc. 30th ACM Int. Conf. Information & Knowledge Management, pp.1243-1252, 2021. 10.1145\/3459637.3482297","DOI":"10.1145\/3459637.3482297"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] S. Park, M. Yoon, J.-W. Lee, H. Park, and J. Lee, \u201cToward a better understanding of loss functions for collaborative filtering,\u201d Proc. 32nd ACM Int. Conf. Information and Knowledge Management, pp.2034-2043, 2023. 10.1145\/3583780.3615086","DOI":"10.1145\/3583780.3615086"},{"key":"17","unstructured":"[17] W. Fan, X. Liu, W. Jin, X. Zhao, J. Tang, and Q. Li, \u201cGraph trend networks for recommendations,\u201d arXiv preprint arXiv:2108.05552, 2021."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J. Choi, J. Jeon, and N. Park, \u201cLt-ocf: learnable-time ode-based collaborative filtering,\u201d Proc. 30th ACM Int. Conf. Information & Knowledge Management, pp.251-260, 2021. 10.1145\/3459637.3482449","DOI":"10.1145\/3459637.3482449"},{"key":"19","unstructured":"[19] I. Chami, Z. Ying, C. R\u00e9, and J. Leskovec, \u201cHyperbolic graph convolutional neural networks,\u201d Advances in Neural Information Processing Systems, vol.32, 2019."},{"key":"20","unstructured":"[20] P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, \u201cSupervised contrastive learning,\u201d Advances in neural information processing systems, vol.33, pp.18661-18673, 2020."},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, \u201cSelf-supervised learning: Generative or contrastive,\u201d IEEE Trans. knowledge and data engineering, vol.35, no.1, pp.857-876, 2021. 10.1109\/tkde.2021.3090866","DOI":"10.1109\/TKDE.2021.3090866"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Yu, H. Yin, X. Xia, T. Chen, L. Cui, and Q.V.H. Nguyen, \u201cAre graph augmentations necessary? simple graph contrastive learning for recommendation,\u201d Proc. 45th international ACM SIGIR conference on research and development in information retrieval, pp.1294-1303, 2022. 10.1145\/3477495.3531937","DOI":"10.1145\/3477495.3531937"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Yu, X. Xia, T. Chen, L. Cui, N.Q.V. Hung, and H. Yin, \u201cXsimgcl: Towards extremely simple graph contrastive learning for recommendation,\u201d IEEE Trans. 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