SE-PEF: a Resource for Personalized Expert Finding

<p>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).<br>The contributed dataset&nbsp; includes more than&nbsp; 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.<br>The results of the preliminary experiments conducted show the appropriateness of&nbsp;SE-PEF to evaluate and to train effective EF models.</p> <p>If you use this dataset, please also cite the following:</p> <p>```</p> <p>@inproceedings{<br>&nbsp; 10.1145/3624918.3625335,<br>&nbsp; author = {Kasela, Pranav and Pasi, Gabriella and Perego, Raffaele},<br>&nbsp; title = {SE-PEF: a Resource for Personalized Expert Finding},<br>&nbsp; year = {2023},<br>&nbsp; isbn = {9798400704086},<br>&nbsp; publisher = {Association for Computing Machinery},<br>&nbsp; address = {New York, NY, USA},<br>&nbsp; url = {https://doi.org/10.1145/3624918.3625335},<br>&nbsp; doi = {10.1145/3624918.3625335},<br>&nbsp; booktitle = {Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region},<br>&nbsp; pages = {288&ndash;309},<br>&nbsp; numpages = {22},<br>&nbsp; series = {SIGIR-AP '23}<br>}</p> <p>```</p>