SEUPD@CLEF: Team NEON. A Memoryless Approach To Longitudinal Evaluation
Contributo in Atti di convegno
Data di Pubblicazione:
2023
Abstract:
This paper presents a possible approach for the LongEval Lab of the CLEF 2023 conference concerning Longitudinal Evaluation of Model Performance. Studies have shown that the performance of Information Retrieval systems decreases as the time gap between the test data and the training data increases. The LongEval Lab focuses on the development of a robust temporal IR system that improves such performance.
Tipologia CRIS:
04.01 - Contributo in atti di convegno
Keywords:
Information Retrieval; LongEval CLEF 2023; Search Engine
Elenco autori:
Bortolin, S.; Ceccon, G.; Czaczkes, G.; Pastore, A.; Renna, P.; Zerbo, G.; Ferro, N.
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Link al Full Text:
Titolo del libro:
CEUR Workshop Proceedings
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