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Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>The increasing specialization and segmentation of modern medical practice, while improving expertise, pose significant challenges in efficiently connecting patients with the right healthcare professionals. The vast array of medical specializations, coupled with sparse data on doctor profiles, overwhelms traditional recommendation algorithms. This study introduces CLEAR-Med: A Contrastive Learning-Enhanced knowledge grAph Recommender designed to match patients with healthcare providers in specific Medical subfields. CLEAR-Med leverages a domain-specific Knowledge Graph (KG) and advanced Contrastive Learning (CL) techniques to capture the nuanced expertise and preferences of doctors, effectively addressing data sparsity and information overload in Online Healthcare Communities (OHCs). The system constructs a comprehensive KG enriched with diverse information, including doctors\u2019 social relationships, professional networks, and specialized attributes derived from OHC data. By embedding key entities and attributes through CL, CLEAR-Med generates robust representations, supported by a flexible attribute encoding module that integrates both efficient LSTMs and powerful Transformer-based models. Its modular prediction layer, featuring options from a stable Multilayer Perceptron (MLP) to an advanced generative diffusion model, then produces highly accurate and personalized recommendation sequences. CLEAR-Med demonstrates superior recommendation performance in baseline comparison experiments, excelling in adaptability and accuracy within OHC settings. Ablation studies confirm the effectiveness of individual components, while further experiments exploring advanced architectures like Transformers and diffusion models highlight the strong balance our framework strikes between performance and computational efficiency. Beyond addressing data sparsity challenges, CLEAR-Med establishes a strong foundation for future advancements in specialized medical matching systems, filling critical research gaps in the domain.<\/jats:p>","DOI":"10.1145\/3797873","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T13:11:03Z","timestamp":1772457063000},"page":"1-62","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Nuanced Differences, Profound Impact: A Comparative Learning-Enhanced Knowledge Graph Recommender for Expert Identification in Specialized Medical Fields"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0866-2546","authenticated-orcid":false,"given":"Hongxun","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3525-6554","authenticated-orcid":false,"given":"Yechi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3367-4470","authenticated-orcid":false,"given":"Xiaonan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7159-0384","authenticated-orcid":false,"given":"Tuo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0183-4504","authenticated-orcid":false,"given":"Wenping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"issue":"6","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"S32","DOI":"10.1097\/MLR.0b013e3181db53a4","article-title":"Electronic patient-reported data capture as a foundation of rapid learning cancer care","volume":"48","author":"Abernethy Amy P.","year":"2010","unstructured":"Amy P. 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ACM, New York, NY, 187\u2013195."},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1145\/3461778.3462100","volume-title":"Proceedings of the 2021 ACM Designing Interactive Systems Conference (Virtual Event) (DIS \u201921)","author":"Gatos Do\u011fa","year":"2021","unstructured":"Do\u011fa Gatos, Asl\u0131 G\u00fcnay, G\u00fcncel K\u0131rlang\u0131\u00e7, Kemal Kuscu, and Asim Evren Yantac. 2021. How HCI bridges health and design in online health communities: A systematic review. In Proceedings of the 2021 ACM Designing Interactive Systems Conference (Virtual Event) (DIS \u201921). ACM, New York, NY, 970\u2013983."},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"15955","DOI":"10.1109\/ACCESS.2019.2894421","article-title":"Towards a knowledge-based recommender system for linking electronic patient records with continuing medical education information at the point of care","volume":"7","author":"Gil Manuel","year":"2019","unstructured":"Manuel Gil, Reem El Sherif, Manon Pluye, Benjamin C. M. Fung, Roland Grad, and Pierre Pluye. 2019. Towards a knowledge-based recommender system for linking electronic patient records with continuing medical education information at the point of care. 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Retrieved from https:\/\/arxiv.org\/abs\/1808.03912"},{"key":"e_1_3_1_38_2","first-page":"173","volume-title":"Proceedings of the 26th International Conference on World Wide Web (WWW \u201917)","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW \u201917). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173\u2013182."},{"key":"e_1_3_1_39_2","first-page":"4806","article-title":"The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling","volume":"8","author":"Ho Yaoshiang","year":"2019","unstructured":"Yaoshiang Ho and Samuel Wookey. 2019. The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling. 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Understanding fairness in recommender systems: A healthcare perspective. In Proceedings of the 18th ACM Conference on Recommender Systems (RecSys \u201924). ACM, New York, NY, 1125\u20131130."},{"key":"e_1_3_1_50_2","unstructured":"Mayank Kejriwal Hamid Haidarian Min-Hsueh Chiu Andy Xiang Deep Shrestha and Faizan Javed. 2023. A knowledge graph-based search engine for robustly finding doctors and locations in the healthcare domain. arXiv:2310.05258. Retrieved from https:\/\/arxiv.org\/abs\/2310.05258"},{"key":"e_1_3_1_51_2","volume-title":"Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI \u201924)","author":"Kim Dajung","year":"2024","unstructured":"Dajung Kim, Niko Vegt, Valentijn Visch, and Marina Bos-De Vos. 2024. How much decision power should (A)I have?: Investigating patients\u2019 preferences towards AI autonomy in healthcare decision making. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI \u201924). ACM, New York, NY, Article 439, 17 pages."},{"key":"e_1_3_1_52_2","first-page":"250","volume-title":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI \u201923)","author":"Kim Sunnie S. Y.","year":"2023","unstructured":"Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, and Andr\u00e9s Monroy-Hern\u00e1ndez. 2023. \u201cHelp me help the AI\u201d: Understanding how explainability can support human-AI interaction. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI \u201923). ACM, New York, NY, Article 250, 17 pages."},{"key":"e_1_3_1_53_2","unstructured":"Thomas Kipf Elise Van der Pol and Max Welling. 2019. Contrastive learning of structured world models. arXiv:1911.12247. Retrieved from https:\/\/arxiv.org\/abs\/1911.12247"},{"key":"e_1_3_1_54_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https:\/\/arxiv.org\/abs\/1609.02907"},{"issue":"4","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s13735-022-00245-6","article-title":"Contrastive self-supervised learning: Review, progress, challenges and future research directions","volume":"11","author":"Kumar Pranjal","year":"2022","unstructured":"Pranjal Kumar, Piyush Rawat, and Siddhartha Chauhan. 2022. Contrastive self-supervised learning: Review, progress, challenges and future research directions. 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Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 30th Innovative Applications of Artificial Intelligence Conference, and 8th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI \u201918\/IAAI \u201918\/EAAI \u201918). AAAI Press, Article 433, 8 pages."},{"key":"e_1_3_1_59_2","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 31st Innovative Applications of Artificial Intelligence Conference, and 9th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI \u201919\/IAAI \u201919\/EAAI \u201919)","author":"Li Zeyu","year":"2019","unstructured":"Zeyu Li, Jyun-Yu Jiang, Yizhou Sun, and Wei Wang. 2019. Personalized question routing via heterogeneous network embedding. 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In Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 556\u2013559."},{"key":"e_1_3_1_62_2","first-page":"1","article-title":"Acceptance prediction for answers on online health-care community","volume":"20","author":"Liu Qianlong","year":"2019","unstructured":"Qianlong Liu, Kangenbei Liao, Kelvin Kam-Fai Tsoi, and Zhongyu Wei. 2019. Acceptance prediction for answers on online health-care community. BMC Bioinformatics 20 (2019), 1\u20138.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"e17","DOI":"10.1016\/S2215-0366(20)30077-8","article-title":"Online mental health services in China during the COVID-19 outbreak","volume":"7","author":"Liu Shuai","year":"2020","unstructured":"Shuai Liu, Lulu Yang, Chenxi Zhang, Yu-Tao Xiang, Zhongchun Liu, Shaohua Hu, and Bin Zhang. 2020. Online mental health services in China during the COVID-19 outbreak. 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Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application 8, 1 (2021), 141\u2013163.","journal-title":"Annual Review of Statistics and Its Application"},{"key":"e_1_3_1_72_2","doi-asserted-by":"crossref","first-page":"104403","DOI":"10.1016\/j.jbi.2023.104403","article-title":"Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study","volume":"143","author":"Murali Lino","year":"2023","unstructured":"Lino Murali, G. Gopakumar, Daleesha M. Viswanathan, and Prema Nedungadi. 2023. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. Journal of Biomedical Informatics 143 (2023), 104403.","journal-title":"Journal of Biomedical Informatics"},{"key":"e_1_3_1_73_2","first-page":"1","volume-title":"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI \u201918)","author":"Nakikj Drashko","year":"2018","unstructured":"Drashko Nakikj and Lena Mamykina. 2018. Lost in migration: Information management and community building in an online health community. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI \u201918). ACM, New York, NY, 1\u201314."},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/2740908.2742748","volume-title":"Proceedings of the 24th International Conference on World Wide Web (WWW \u201915 Companion)","author":"Narducci Fedelucio","year":"2015","unstructured":"Fedelucio Narducci, Cataldo Musto, Marco Polignano, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2015. 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Retrieved from https:\/\/arxiv.org\/abs\/1905.10947"},{"issue":"1","key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TASE.2018.2839651","article-title":"Dynamic recommendation of physician assortment with patient preference learning","volume":"16","author":"Pan Xin","year":"2018","unstructured":"Xin Pan, Jie Song, and Fan Zhang. 2018. Dynamic recommendation of physician assortment with patient preference learning. IEEE Transactions on Automation Science and Engineering 16, 1 (2018), 115\u2013126.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"10","key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1097\/ACM.0000000000003555","article-title":"When a specialty becomes \u201cwomen\u2019s work\u201d: Trends in and implications of specialty gender segregation in medicine","volume":"95","author":"Pelley Elaine","year":"2020","unstructured":"Elaine Pelley and Molly Carnes. 2020. 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ACM, New York, NY, 2131\u20132140."},{"key":"e_1_3_1_81_2","first-page":"2288","volume-title":"Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI \u201924)","author":"Peng Qiyao","year":"2024","unstructured":"Qiyao Peng, Wenjun Wang, Hongtao Liu, Cuiying Huo, and Minglai Shao. 2024. Graph collaborative expert finding with contrastive learning. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI \u201924). Kate Larson (Ed.), International Joint Conferences on Artificial Intelligence Organization, 2288\u20132296."},{"key":"e_1_3_1_82_2","doi-asserted-by":"crossref","unstructured":"Qiyao Peng Hongyan Xu Yinghui Wang Hongtao Liu Cuiying Huo and Wenjun Wang. 2024. PEPT: Expert finding meets personalized pre-training. ACM Transactions on Information Systems 43 Article 8 (Nov. 2024) 26 pages.","DOI":"10.1145\/3690380"},{"key":"e_1_3_1_83_2","first-page":"1150","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Virtual Event) (KDD \u201920)","author":"Qiu Jiezhong","year":"2020","unstructured":"Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020. GCC: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Virtual Event) (KDD \u201920). ACM, New York, NY, 1150\u20131160."},{"issue":"12","key":"e_1_3_1_84_2","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar Alvin","year":"2018","unstructured":"Alvin Rajkomar, Michaela Hardt, Michael D. 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Journal of Health Communication 21, 12 (2016), 1251\u20131259.","journal-title":"Journal of Health Communication"},{"key":"e_1_3_1_87_2","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1145\/3336191.3371831","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM \u201920)","author":"Shenbin Ilya","year":"2020","unstructured":"Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko. 2020. RecVAE: A new variational autoencoder for top-N recommendations with implicit feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM \u201920). ACM, New York, NY, 528\u2013536."},{"issue":"7","key":"e_1_3_1_88_2","doi-asserted-by":"crossref","first-page":"e240","DOI":"10.2196\/jmir.9300","article-title":"Evaluating doctor performance: Ordinal regression-based approach","volume":"20","author":"Shi Yong","year":"2018","unstructured":"Yong Shi, Peijia Li, Xiaodan Yu, Huadong Wang, and Lingfeng Niu. 2018. Evaluating doctor performance: Ordinal regression-based approach. Journal of Medical Internet Research 20, 7 (2018), e240.","journal-title":"Journal of Medical Internet Research"},{"issue":"7","key":"e_1_3_1_89_2","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1056\/NEJMp1000448","article-title":"Looking back, moving forward","volume":"362","author":"Skinner Jonathan","year":"2010","unstructured":"Jonathan Skinner, Douglas Staiger, and Elliott S. Fisher. 2010. Looking back, moving forward. 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The Medical Journal of Australia 201, S1 (2014), S26\u2013S28.","journal-title":"The Medical Journal of Australia"},{"issue":"1","key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11606-010-1453-3","article-title":"How well do doctors know their patients? Factors affecting physician understanding of patients\u2019 health beliefs","volume":"26","author":"Street Richard L.","year":"2011","unstructured":"Richard L. Street, Jr. and Paul Haidet. 2011. How well do doctors know their patients? Factors affecting physician understanding of patients\u2019 health beliefs. Journal of General Internal Medicine 26, 1 (2011), 21\u201327.","journal-title":"Journal of General Internal Medicine"},{"issue":"19","key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"e38184","DOI":"10.2196\/38184","article-title":"Development and evaluation of health recommender systems: Systematic scoping review and evidence mapping","volume":"25","author":"Sun Yue","year":"2023","unstructured":"Yue Sun, Jia Zhou, Mengmeng Ji, Lusi Pei, and Zhiwen Wang. 2023. Development and evaluation of health recommender systems: Systematic scoping review and evidence mapping. Journal of Medical Internet Research 25, 19 (Jan. 2023), e38184.","journal-title":"Journal of Medical Internet Research"},{"key":"e_1_3_1_94_2","unstructured":"Zhiqing Sun Zhi-Hong Deng Jian-Yun Nie and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv:1902.10197. Retrieved from https:\/\/arxiv.org\/abs\/1902.10197"},{"issue":"2","key":"e_1_3_1_95_2","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/TETC.2014.2386133","article-title":"Discover the expert: Context-adaptive expert selection for medical diagnosis","volume":"3","author":"Tekin Cem","year":"2014","unstructured":"Cem Tekin, Onur Atan, and Mihaela Van Der Schaar. 2014. Discover the expert: Context-adaptive expert selection for medical diagnosis. IEEE Transactions on Emerging Topics in Computing 3, 2 (2014), 220\u2013234.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"e_1_3_1_96_2","doi-asserted-by":"crossref","unstructured":"Yijun Tian Chuxu Zhang Zhichun Guo Chao Huang Ronald Metoyer and Nitesh V. Chawla. 2022. RecipeRec: A heterogeneous graph learning model for recipe recommendation. arXiv:2205.14005. 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AAAI Press, Article 37, 8 pages."},{"key":"e_1_3_1_100_2","unstructured":"Th\u00e9o Trouillon Johannes Welbl Sebastian Riedel \u00c9ric Gaussier and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. arXiv:1606.06357. Retrieved from https:\/\/arxiv.org\/abs\/1606.06357"},{"key":"e_1_3_1_101_2","first-page":"6000","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS \u201917)","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS \u201917). Curran Associates, Inc., Red Hook, NY, 6000\u20136010."},{"key":"e_1_3_1_102_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903. Retrieved from https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/BF02598977","article-title":"Balkanization of medical specialties","volume":"6","author":"Waldman Robert H.","year":"1991","unstructured":"Robert H. Waldman. 1991. Balkanization of medical specialties. Journal of General Internal Medicine 6 (1991), 265\u2013265.","journal-title":"Journal of General Internal Medicine"},{"key":"e_1_3_1_104_2","first-page":"535","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923)","author":"Wang Hao","year":"2023","unstructured":"Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, and Zhiyuan Liu. 2023. Knowledge-adaptive contrastive learning for recommendation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923). ACM, New York, NY, 535\u2013543."},{"key":"e_1_3_1_105_2","first-page":"417","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM \u201918)","author":"Wang Hongwei","year":"2018","unstructured":"Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. RippleNet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM \u201918). ACM, New York, NY, 417\u2013426."},{"key":"e_1_3_1_106_2","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1145\/3292500.3330836","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201919)","author":"Wang Hongwei","year":"2019","unstructured":"Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019. 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ACM, New York, NY, 2000\u20132010."},{"key":"e_1_3_1_108_2","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1145\/3308558.3313417","volume-title":"Proceedings of the World Wide Web Conference (WWW \u201919)","author":"Wang Hongwei","year":"2019","unstructured":"Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In Proceedings of the World Wide Web Conference (WWW \u201919). ACM, New York, NY, 3307\u20133313."},{"key":"e_1_3_1_109_2","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1145\/3292500.3330989","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201919)","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201919). ACM, New York, NY, 950\u2013958."},{"issue":"3","key":"e_1_3_1_110_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3383780","article-title":"Predicting user posting activities in online health communities with deep learning","volume":"11","author":"Wang Xiangyu","year":"2020","unstructured":"Xiangyu Wang, Kang Zhao, Xun Zhou, and Nick Street. 2020. Predicting user posting activities in online health communities with deep learning. 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ACM, New York, NY, 5150\u20135161."},{"issue":"5","key":"e_1_3_1_112_2","doi-asserted-by":"crossref","first-page":"103773","DOI":"10.1016\/j.ipm.2024.103773","article-title":"Deep expertise and interest personalized transformer for expert finding","volume":"61","author":"Wang Yinghui","year":"2024","unstructured":"Yinghui Wang, Qiyao Peng, Hongtao Liu, Hongyan Xu, Minglai Shao, and Wenjun Wang. 2024. Deep expertise and interest personalized transformer for expert finding. Information Processing & Management 61, 5 (2024), 103773.","journal-title":"Information Processing & Management"},{"key":"e_1_3_1_113_2","first-page":"502","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922)","author":"Wang Yaqing","year":"2022","unstructured":"Yaqing Wang, Song Wang, Yanyan Li, and Dejing Dou. 2022. Recognizing medical search query intent by few-shot learning. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922). ACM, New York, NY, 502\u2013512."},{"key":"e_1_3_1_114_2","first-page":"1112","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI \u201914)","author":"Wang Zhen","year":"2014","unstructured":"Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI \u201914). AAAI Press, 1112\u20131119."},{"issue":"9","key":"e_1_3_1_115_2","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1080\/0144929X.2019.1625441","article-title":"An adaptive doctor-recommender system","volume":"38","author":"Waqar Muhammad","year":"2019","unstructured":"Muhammad Waqar, Nadeem Majeed, Hassan Dawood, Ali Daud, and Naif Radi Aljohani. 2019. An adaptive doctor-recommender system. Behaviour & Information Technology 38, 9 (2019), 959\u2013973.","journal-title":"Behaviour & Information Technology"},{"issue":"10231","key":"e_1_3_1_116_2","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1016\/S0140-6736(20)30818-7","article-title":"Virtual health care in the era of COVID-19","volume":"395","author":"Webster Paul","year":"2020","unstructured":"Paul Webster. 2020. Virtual health care in the era of COVID-19. The Lancet 395, 10231 (2020), 1180\u20131181.","journal-title":"The Lancet"},{"key":"e_1_3_1_117_2","first-page":"1120","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining (Virtual Event) (WSDM \u201922)","author":"Wei Wei","year":"2022","unstructured":"Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022. Contrastive meta learning with behavior multiplicity for recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (Virtual Event) (WSDM \u201922). ACM, New York, NY, 1120\u20131128."},{"issue":"2","key":"e_1_3_1_118_2","first-page":"886","article-title":"Physician recommendation on healthcare appointment platforms considering patient choice","volume":"17","author":"Wen Hanqi","year":"2019","unstructured":"Hanqi Wen, Jie Song, and Xin Pan. 2019. Physician recommendation on healthcare appointment platforms considering patient choice. IEEE Transactions on Automation Science and Engineering 17, 2 (2019), 886\u2013899.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"2","key":"e_1_3_1_119_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1145\/2568388.2568405","article-title":"Report on the SIGIR 2013 workshop on health search and discovery","volume":"47","author":"White Ryen W.","year":"2013","unstructured":"Ryen W. White, Elad Yom-Tov, Eric Horvitz, Eugene Agichtein, and William Hersh. 2013. Report on the SIGIR 2013 workshop on health search and discovery. ACM SIGIR Forum 47, 2 (Jan. 2013), 101\u2013108.","journal-title":"ACM SIGIR Forum"},{"key":"e_1_3_1_120_2","doi-asserted-by":"crossref","first-page":"1302844","DOI":"10.3389\/fmed.2023.1302844","article-title":"Applying precision medicine principles to the management of multimorbidity: The utility of comorbidity networks, graph machine learning, and knowledge graphs","volume":"10","author":"Woodman Richard John","year":"2024","unstructured":"Richard John Woodman, Bogda Koczwara, and Arduino Aleksander Mangoni. 2024. Applying precision medicine principles to the management of multimorbidity: The utility of comorbidity networks, graph machine learning, and knowledge graphs. 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Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Proceedings of Machine Learning Research, Vol. 97, PMLR, 6861\u20136871."},{"key":"e_1_3_1_122_2","first-page":"726","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event) (SIGIR \u201921)","author":"Wu Jiancan","year":"2021","unstructured":"Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event) (SIGIR \u201921). ACM, New York, NY, 726\u2013735."},{"issue":"1","key":"e_1_3_1_123_2","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2020), 4\u201324.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_124_2","first-page":"496","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201923)","author":"Xu Jingcao","year":"2023","unstructured":"Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, and Kun Gai. 2023. Multi-behavior self-supervised learning for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201923). ACM, New York, NY, 496\u2013505."},{"key":"e_1_3_1_125_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv:1810.00826. Retrieved from https:\/\/arxiv.org\/abs\/1810.00826"},{"key":"e_1_3_1_126_2","first-page":"3203","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI \u201917)","author":"Xue Hong-Jian","year":"2017","unstructured":"Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI \u201917). AAAI Press, 3203\u20133209."},{"key":"e_1_3_1_127_2","first-page":"2311","volume-title":"Proceedings of the 30th ACM International Conference on Information and Knowledge Management (Virtual Event) (CIKM \u201921)","author":"Yang Han","year":"2021","unstructured":"Han Yang and Junfei Liu. 2021. Knowledge graph representation learning as groupoid: Unifying TransE, RotatE, QuatE, ComplEx. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (Virtual Event) (CIKM \u201921). ACM, New York, NY, 2311\u20132320."},{"issue":"12","key":"e_1_3_1_128_2","doi-asserted-by":"crossref","first-page":"8829","DOI":"10.1109\/TKDE.2024.3466530","article-title":"Multi-level graph knowledge contrastive learning","volume":"36","author":"Yang Haoran","year":"2024","unstructured":"Haoran Yang, Yuhao Wang, Xiangyu Zhao, Hongxu Chen, Hongzhi Yin, Qing Li, and Guandong Xu. 2024. Multi-level graph knowledge contrastive learning. IEEE Transactions on Knowledge and Data Engineering 36, 12 (Dec. 2024), 8829\u20138841.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_129_2","first-page":"1434","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922)","author":"Yang Yuhao","year":"2022","unstructured":"Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922). ACM, New York, NY, 1434\u20131443."},{"issue":"4","key":"e_1_3_1_130_2","first-page":"1","article-title":"Ontology-aware prescription recommendation in treatment pathways using multi-evidence healthcare data","volume":"41","author":"Yao Zijun","year":"2023","unstructured":"Zijun Yao, Bin Liu, Fei Wang, Daby Sow, and Ying Li. 2023. 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Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1\u201322.","journal-title":"Proceedings of the ACM on Human-Computer Interaction"},{"key":"e_1_3_1_132_2","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 5812\u20135823.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_133_2","first-page":"3331","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Zeng Xingshan","unstructured":"Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, and Kam-Fai Wong. 2020. Dynamic online conversation recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (Eds.), Association for Computational Linguistics, 3331\u20133341."},{"key":"e_1_3_1_134_2","first-page":"353","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916)","author":"Zhang Fuzheng","year":"2016","unstructured":"Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916). ACM, New York, NY, 353\u2013362."},{"key":"e_1_3_1_135_2","doi-asserted-by":"crossref","first-page":"65333","DOI":"10.1109\/ACCESS.2018.2875677","article-title":"Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record","volume":"6","author":"Zhang Jinghe","year":"2018","unstructured":"Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, and Laura E. Barnes. 2018. Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access 6 (2018), 65333\u201365346.","journal-title":"IEEE Access"},{"key":"e_1_3_1_136_2","unstructured":"Qingheng Zhang Zequn Sun Wei Hu Muhao Chen Lingbing Guo and Yuzhong Qu. 2019. Multi-view knowledge graph embedding for entity alignment. arXiv:1906.02390. Retrieved from https:\/\/arxiv.org\/abs\/1906.02390"},{"issue":"1","key":"e_1_3_1_137_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3285029","article-title":"Deep learning based recommender system: A survey and new perspectives","volume":"52","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys 52, 1 (2019), 1\u201338.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_138_2","doi-asserted-by":"crossref","first-page":"4706","DOI":"10.1145\/3589334.3648157","volume-title":"Proceedings of the ACM Web Conference 2024 (WWW \u201924)","author":"Zhang Xi Sheryl","year":"2024","unstructured":"Xi Sheryl Zhang, Weifan Guan, Jiahao Lu, Zhaopeng Qiu, Jian Cheng, Xian Wu, and Yefeng Zheng. 2024. GraphLeak: Patient record leakage through gradients with knowledge graph. In Proceedings of the ACM Web Conference 2024 (WWW \u201924). ACM, New York, NY, 4706\u20134716."},{"key":"e_1_3_1_139_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2015.12.001","article-title":"iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization","volume":"66","author":"Zhang Yin","year":"2017","unstructured":"Yin Zhang, Min Chen, Dijiang Huang, Di Wu, and Yong Li. 2017. iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems 66 (2017), 30\u201335.","journal-title":"Future Generation Computer Systems"},{"issue":"1","key":"e_1_3_1_140_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3527662","article-title":"Knowledge-enhanced attributed multi-task learning for medicine recommendation","volume":"41","author":"Zhang Yingying","year":"2023","unstructured":"Yingying Zhang, Xian Wu, Quan Fang, Shengsheng Qian, and Changsheng Xu. 2023. 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ACM, New York, NY, 533\u2013542."},{"key":"e_1_3_1_142_2","first-page":"4592","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922)","author":"Zheng Zhi","year":"2022","unstructured":"Zhi Zheng, Zhaopeng Qiu, Hui Xiong, Xian Wu, Tong Xu, Enhong Chen, and Xiangyu Zhao. 2022. DDR: Dialogue based doctor recommendation for online medical service. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922). ACM, New York, NY, 4592\u20134600."},{"key":"e_1_3_1_143_2","first-page":"1358","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922)","author":"Zou Ding","year":"2022","unstructured":"Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level cross-view contrastive learning for knowledge-aware recommender system. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201922). ACM, New York, NY, 1358\u20131368."},{"key":"e_1_3_1_144_2","first-page":"2817","volume-title":"Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM \u201922)","author":"Zou Ding","year":"2022","unstructured":"Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, and Dangyang Chen. 2022. Improving knowledge-aware recommendation with multi-level interactive contrastive learning. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM \u201922). 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