Document - SE-PEF: a resource for personalized expert finding

2023

Conference article Open Access

SE-PEF: a resource for personalized expert finding

Kasela P., Pasi G., Perego R.

Expert finding

The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets to support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the Expert Finding (EF) task. The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.

Source: SIGIR-AP '23: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 288–309, Beijing, China, 26-28/11/2023

Publisher: ACM Press, New York, USA

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@inproceedings{oai:it.cnr:prodotti:489510,
	title = {SE-PEF: a resource for personalized expert finding},
	author = {Kasela P. and Pasi G. and Perego R.},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/3624918.3625335},
	booktitle = {SIGIR-AP '23: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 288–309, Beijing, China, 26-28/11/2023},
	year = {2023}
}