SE-PEF: a Resource for Personalized Expert Finding

TL;DR

This paper introduces SE-PEF, a large annotated dataset from StackExchange designed to support the development and evaluation of personalized expert finding models in information retrieval.

Contribution

The paper provides a publicly available, richly annotated dataset for personalized expert finding, addressing the lack of suitable resources for comparative evaluation.

Findings

SE-PEF is suitable for training EF models.

Preliminary experiments validate the dataset's effectiveness.

Rich social interaction features enhance model evaluation.

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 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.