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Featured 2019 Journal Article Consumer health search on the web: study of web page understandability and its integration in ranking algorithmsPalotti, Joao, Zuccon, Guido and Hanbury, Allan (2019). Consumer health search on the web: study of web page understandability and its integration in ranking algorithms. Journal of Medical Internet Research, 21 (1) e10986, e10986. doi: 10.2196/10986 |
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Featured 2018 Journal Article Payoffs and pitfalls in using knowledge-bases for consumer health searchJimmy, Zuccon, Guido and Koopman, Bevan (2018). Payoffs and pitfalls in using knowledge-bases for consumer health search. Information Retrieval Journal, 22 (3-4), 350-394. doi: 10.1007/s10791-018-9344-z |
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Featured 2018 Journal Article Recursive module extraction using louvain and pagerankPerrin, Dimitri and Zuccon, Guido (2018). Recursive module extraction using louvain and pagerank. F1000Research, 7 (1286) 1286, 1-11. doi: 10.12688/f1000research.15845.1 |
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2024 Conference Publication Dense retrieval with continuous explicit feedback for systematic review screening prioritisationMao, Xinyu, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2024). Dense retrieval with continuous explicit feedback for systematic review screening prioritisation. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657921 |
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2024 Conference Publication FeB4RAG: evaluating federated search in the context of retrieval augmented generationWang, Shuai, Khramtsova, Ekaterina, Zhuang, Shengyao and Zuccon, Guido (2024). FeB4RAG: evaluating federated search in the context of retrieval augmented generation. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657853 |
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2024 Conference Publication A setwise approach for effective and highly efficient zero-shot ranking with large language modelsZhuang, Shengyao, Zhuang, Honglei, Koopman, Bevan and Zuccon, Guido (2024). A setwise approach for effective and highly efficient zero-shot ranking with large language models. SIGIR ’24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657813 |
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2024 Conference Publication Evaluating generative ad hoc information retrievalGienapp, Lukas, Scells, Harrisen, Deckers, Niklas, Bevendorff, Janek, Wang, Shuai, Kiesel, Johannes, Syed, Shahbaz, Fröbe, Maik, Zuccon, Guido, Stein, Benno, Hagen, Matthias and Potthast, Martin (2024). Evaluating generative ad hoc information retrieval. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieva, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657849 |
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2024 Conference Publication Embark on DenseQuest: a system for selecting the best dense retriever for a custom collectionKhramtsova, Ekaterina, Leelanupab, Teerapong, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657674 |
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2024 Conference Publication Large language models based stemming for information retrieval: promises, pitfalls and failuresWang, Shuai, Zhuang, Shengyao and Zuccon, Guido (2024). Large language models based stemming for information retrieval: promises, pitfalls and failures. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657949 |
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2024 Conference Publication Revisiting document expansion and filtering for effective first-stage retrievalMansour, Watheq, Zhuang, Shengyao, Zuccon, Guido and Mackenzie, Joel (2024). Revisiting document expansion and filtering for effective first-stage retrieval. SIGIR '24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657850 |
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2024 Journal Article ACM SIGIR 2024 Chairs' WelcomeHui Yang, Grace, Wang, Hongning, Han, Sam, Hauff, Claudia, Zuccon, Guido and Zhang, Yi (2024). ACM SIGIR 2024 Chairs' Welcome. SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, iii-v. doi: 10.1145/3626772 |
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2024 Conference Publication Leveraging LLMs for unsupervised dense retriever rankingKhramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Leveraging LLMs for unsupervised dense retriever ranking. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657798 |
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2024 Journal Article The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physiciansScott, Ian A. and Zuccon, Guido (2024). The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians. Internal Medicine Journal, 54 (5), 705-715. doi: 10.1111/imj.16393 |
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2024 Conference Publication CoLAL: Co-learning active learning for text classificationLe, Linh, Zhao, Genghong, Zhang, Xia, Zuccon, Guido and Demartini, Gianluca (2024). CoLAL: Co-learning active learning for text classification. Thirty-Eighth AAAI Conference on Artificial Intelligence, Vancouver, BC Canada, 20–27 February 2024. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i12.29235 |
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2024 Book Chapter How to Forget Clients in Federated Online Learning to Rank?Wang, Shuyi, Liu, Bing and Zuccon, Guido (2024). How to Forget Clients in Federated Online Learning to Rank?. Lecture Notes in Computer Science. (pp. 105-121) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-56063-7_7 |
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2024 Book Chapter A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARMao, Xinyu, Koopman, Bevan and Zuccon, Guido (2024). A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR. Lecture Notes in Computer Science. (pp. 132-146) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-56066-8_13 |
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2024 Conference Publication Stochastic Featurization for Active LearningLe, Linh, Nguyen, Minh-Tien, Tran, Khai Phan, Zhao, Genghong, Xia, Zhang, Zuccon, Guido and Demartini, Gianluca (2024). Stochastic Featurization for Active Learning. Second International Workshop, TAI4H 2024, Jeju, South Korea, 4 August 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-67751-9_5 |
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2024 Conference Publication Zero-shot generative large language models for systematic review screening automationWang, Shuai, Scells, Harrisen, Zhuang, Shengyao, Potthast, Martin, Koopman, Bevan and Zuccon, Guido (2024). Zero-shot generative large language models for systematic review screening automation. 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, United Kingdom, 24-28 March 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-56027-9_25 |
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2023 Journal Article Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix itvan der Vegt, Anton, Campbell, Victoria and Zuccon, Guido (2023). Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it. Medical Journal of Australia, 220 (4), 172-175. doi: 10.5694/mja2.52195 |
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2023 Conference Publication Selecting which dense retriever to use for zero-shot searchKhramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa, Wang, Xi and Zuccon, Guido (2023). Selecting which dense retriever to use for zero-shot search. SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Beijing, China, 26-28 November 2023. New York, United States: Association for Computing Machinery. doi: 10.1145/3624918.3625330 |
