The problem of personalization in Information Retrieval has been under study for a long time. A well know issue related to this task is the lack of publicly available datasets that can support a comparative evaluation of personalised 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 task of Expert Finding (EF).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. If you use this dataset, please also cite the following: ``` @inproceedings{ 10.1145/3624918.3625335, author = {Kasela, Pranav and Pasi, Gabriella and Perego, Raffaele}, title = {SE-PEF: a Resource for Personalized Expert Finding}, year = {2023}, isbn = {9798400704086}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3624918.3625335}, doi = {10.1145/3624918.3625335}, booktitle = {Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region}, pages = {288–309}, numpages = {22}, series = {SIGIR-AP '23}} ```