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：个性化检索问题在信息检索领域已研究多年。与此任务相关的一个众所周知的问题是：缺乏可公开获取、支持个性化搜索系统比较评估的数据集。为促进该领域发展，本文提出SE-PEF（StackExchange - 个性化专家发现），一种用于设计和评估专家发现任务（EF）个性化模型的资源。该数据集包含来自3306位专家的超过25万次查询和56.5万条答案，并标注了丰富的特征集，用以建模流行cQA平台用户间的社交互动。初步实验结果表明，SE-PEF适用于评估和训练有效的专家发现模型。