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 that can 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 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.

翻译：信息检索中的个性化问题已得到长期研究。与这一任务相关的众所周知的问题在于缺乏支持个性化搜索系统比较评估的公开可用数据集。为弥补这一不足，本文介绍了SE-PEF（StackExchange - 个性化专家发现），一个可用于设计与评估专家发现（EF）任务相关个性化模型的资源。该数据集包含来自3306位专家的超过25万条查询和56.5万个答案，并标注了丰富的特征集，这些特征模拟了一个流行cQA平台用户之间的社交互动。初步实验结果表明，SE-PEF适用于评估和训练高效的EF模型。