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.

Publisher: ACM Press

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BibTeX entry

@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},
	year = {2023}
}