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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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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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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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2025 Journal Article The epidemiology of hospitalisations from four key environmentally sensitive zoonotic diseases in Queensland, 2012–2019Proboste, Tatiana, Lau, Colleen L., Clark, Nicholas, Jagals, Paul, Sly, Peter D., Lambert, Stephen B., Devine, Gregor, Zuccon, Guido and Soares Magalhães, Ricardo J. (2025). The epidemiology of hospitalisations from four key environmentally sensitive zoonotic diseases in Queensland, 2012–2019. Tropical Medicine and International Health. doi: 10.1111/tmi.14139 |
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2025 Conference Publication ReSLLM: large language models are strong resource selectors for federated searchWang, Shuai, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). ReSLLM: large language models are strong resource selectors for federated search. The ACM Web Conference 2025, Sydney, NSW Australia, 28 April-2 May 2025. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3701716.3715595 |
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2025 Conference Publication An investigation of prompt variations for zero-shot LLM-based rankersSun, Shuoqi, Zhuang, Shengyao, Wang, Shuai and Zuccon, Guido (2025). An investigation of prompt variations for zero-shot LLM-based rankers. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88711-6_12 |
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2025 Conference Publication Corpus subsampling: estimating the effectiveness of neural retrieval models on large corporaFröbe, Maik, Parry, Andrew, Scells, Harrisen, Wang, Shuai, Zhuang, Shengyao, Zuccon, Guido, Potthast, Martin and Hagen, Matthias (2025). Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88708-6_29 |
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2024 Conference Publication Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval SystemsZhuang, Shengyao, Koopman, Bevan, Chu, Xiaoran and Zuccon, Guido (2024). Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698414 |
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2024 Conference Publication Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-pointsSabei, Ismail, Galal, Mahmoud, Koopman, Bevan and Zuccon, Guido (2024). Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698417 |
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2024 Conference Publication PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrievalZhuang, Shengyao, Ma, Xueguang, Koopman, Bevan, Lin, Jimmy and Zuccon, Guido (2024). PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval. 29th Conference on Empirical Methods in Natural Language Processing, Miami, FL USA, 12-16 November 2024. Stroudsberg, PA USA: Association for Computational Linguistics. doi: 10.18653/v1/2024.emnlp-main.250 |
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2024 Conference Publication Source-Free Domain-Invariant Performance PredictionKhramtsova, Ekaterina, Baktashmotlagh, Mahsa, Zuccon, Guido, Wang, Xi and Salzmann, Mathieu (2024). Source-Free Domain-Invariant Performance Prediction. 18th European Conference on Computer Vision ECCV 2024, Milan, Italy, 29 September – 4 October 2024. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-72989-8_6 |
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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 |
