Overview of the PIR Track at FIRE 2024: Evaluation of Personalised Information Retrieval

This abstract provides a short overview of the first edition of the shared task on Personalised Information Retrieval (PIR) organized at the 16th Forum for Information Retrieval Evaluation (FIRE 2024). A more detailed discussion of the approaches used by the participating teams is available in the track overview paper. PIR 2024 consisted of two sub-tasks. The first sub-task aims to explore the personalisation in cQA based on user profiles, following the standard IR pipeline. The second one, instead, aims to investigate the personalisation in cQA based on user profiles using recent LLMs and prompt engineering. Although the tasks saw an enthusiastic response in registrations, with 10 teams requesting the dataset, only 1 team finally submitted the runs, and 2 of them submitted the working notes.

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Published
2024-12-12
DOI
https://doi.org/10.1145/3734947.3735665
Primary Topic
Semantic Web and Ontologies
Type
article
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Overview of the PIR Track at FIRE 2024: Evaluation of Personalised Information Retrieval

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Overview of the PIR Track at FIRE 2024: Evaluation of Personalised Information Retrieval

Marco Braga, Gian Carlo Milanese, Sandip Modha, Alessandro Raganato, Pranav Kasela, Gabriella Pasi, Marco Viviani, Georgios Peikos, Effrosyni Sokli
article en

Abstract

This abstract provides a short overview of the first edition of the shared task on Personalised Information Retrieval (PIR) organized at the 16th Forum for Information Retrieval Evaluation (FIRE 2024). A more detailed discussion of the approaches used by the participating teams is available in the track overview paper. PIR 2024 consisted of two sub-tasks. The first sub-task aims to explore the personalisation in cQA based on user profiles, following the standard IR pipeline. The second one, instead, aims to investigate the personalisation in cQA based on user profiles using recent LLMs and prompt engineering. Although the tasks saw an enthusiastic response in registrations, with 10 teams requesting the dataset, only 1 team finally submitted the runs, and 2 of them submitted the working notes.

University of Milano-Bicocca (IT)
Openalex Percentile: Top 72%
Semantic Web and Ontologies
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