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.

Kasela, P., Pasi, G., Perego, R. (2023). SE-PEF: a Resource for Personalized Expert Finding. In SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp.288-293). Association for Computing Machinery, Inc [10.1145/3624918.3625335].

SE-PEF: a Resource for Personalized Expert Finding

Kasela P.
;
Pasi G.;
2023

Abstract

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.
paper
Expert Finding; Personalization; Question Answering; User Model;
English
11th International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2023 - 26 November 2023 through 28 November 2023
2023
Ai, Q; Liu, Y; Moffat, A; Huang, X; Sakai, T; Zobel, J
SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region
9798400704086
2023
288
293
none
Kasela, P., Pasi, G., Perego, R. (2023). SE-PEF: a Resource for Personalized Expert Finding. In SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp.288-293). Association for Computing Machinery, Inc [10.1145/3624918.3625335].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454388
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