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个人简介
My research work has been focused on performance prediction for Information Retrieval Systems:
In many engineering fields, it is possible to mathematically and statistically compute ahead the expected behaviour and performance of a system so that it can be designed and developed to achieve such performance. For example, a bridge is built keeping in mind the expected weight it will have to endure. This important feature is missing in systems where the human cognitive effort is at stake, such as Information Retrieval (IR): the human component makes hard to predict the performance achieved by IR systems before they are actually developed, deployed, and tested. Thus evaluation procedures require expensive user studies or are applied on toy problems and might lack generalization capability when the system is used in real-world scenarios. Yet, the research reached a consensus on the fact that it is possible to develop models to gain insight on the system's performance. With my research work, we aim at identifying and develop models capable of bounding and predicting the IR system performance, without relying on a post-hoc evaluation phase. Secondly, we plan to identify features of collections and data sets, tasks, users, and models, that can be efficiently gathered and effectively leveraged by the predictive model.
In many engineering fields, it is possible to mathematically and statistically compute ahead the expected behaviour and performance of a system so that it can be designed and developed to achieve such performance. For example, a bridge is built keeping in mind the expected weight it will have to endure. This important feature is missing in systems where the human cognitive effort is at stake, such as Information Retrieval (IR): the human component makes hard to predict the performance achieved by IR systems before they are actually developed, deployed, and tested. Thus evaluation procedures require expensive user studies or are applied on toy problems and might lack generalization capability when the system is used in real-world scenarios. Yet, the research reached a consensus on the fact that it is possible to develop models to gain insight on the system's performance. With my research work, we aim at identifying and develop models capable of bounding and predicting the IR system performance, without relying on a post-hoc evaluation phase. Secondly, we plan to identify features of collections and data sets, tasks, users, and models, that can be efficiently gathered and effectively leveraged by the predictive model.
研究兴趣
论文共 53 篇作者统计合作学者相似作者
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European Conference on Advances in Information Retrievalpp.278-294, (2024)
Italian Research Conference on Digital Library Management Systemspp.41-46, (2024)
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Guglielmo Faggioli,Laura Dietz, Charles Clarke,Gianluca Demartini,Matthias Hagen,Claudia Hauff, Noriko Kando,Evangelos Kanoulas,Martin Potthast,Benno Stein,Henning Wachsmuth
Communications of the ACM (2024)
PROCEEDINGS OF THE 2023 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2023 (2023): 39-50
PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023pp.1355-1365, (2023)
CLEFpp.343-369, (2023)
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PROCEEDINGS OF THE 2023 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2023pp.51-63, (2023)
SIGIR Forumno. 2 (2023): 8:1-8:12
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