SE-PEF: a Resource for Personalized Expert Finding-Reference-Cited by-同舟云学术

SE-PEF: a Resource for Personalized Expert Finding

Published:2023-11-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region
language:
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Author:

Kasela Pranav¹^ORCID,Pasi Gabriella¹^ORCID,Perego Raffaele²^ORCID

Affiliation:

1. Department of Informatics, Systems and Communication - DISCo, University of Milano-Bicocca, Italy

2. ISTI-CNR, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624918.3625335

Reference22 articles.

1. Arian Askari , Suzan Verberne , and Gabriella Pasi . 2022 . Expert Finding in Legal Community Question Answering. In European Conference on Information Retrieval. Springer, Springer International Publishing, Cham, 22–30 . Arian Askari, Suzan Verberne, and Gabriella Pasi. 2022. Expert Finding in Legal Community Question Answering. In European Conference on Information Retrieval. Springer, Springer International Publishing, Cham, 22–30.

2. Elias Bassani . 2022. ranx: A Blazing-Fast Python Library for Ranking Evaluation and Comparison . In ECIR (2)(Lecture Notes in Computer Science, Vol. 13186) . Springer , Cham , 259–264. Elias Bassani. 2022. ranx: A Blazing-Fast Python Library for Ranking Evaluation and Comparison. In ECIR (2)(Lecture Notes in Computer Science, Vol. 13186). Springer, Cham, 259–264.

3. Elias Bassani Pranav Kasela and Gabriella Pasi. 2023. Denoising Attention for Query-aware User Modeling in Personalized Search. arxiv:2308.15968 [cs.IR] Elias Bassani Pranav Kasela and Gabriella Pasi. 2023. Denoising Attention for Query-aware User Modeling in Personalized Search. arxiv:2308.15968 [cs.IR]

4. A Multi-Domain Benchmark for Personalized Search Evaluation

5. ranx.fuse: A Python Library for Metasearch