Dual-View Self-Supervised Pre-Training for Expert Finding

Abstract

Expert finding plays a crucial role in Community Question Answering platforms by routing questions to the most suitable answerers. The key challenge lies in learning high-quality representations for questions and experts. Most existing methods rely on limited supervised signals such as expert–question interactions, and typically focus on modeling only one side of the expert–question pair, suffering from data sparsity and incomplete representation. In this paper, we propose a Dual-view Self-Supervised pre-training framework for Expert Finding (SSEF) that simultaneously pre-trains expert and question representations from large-scale unlabeled data. Specifically, on the expert view, we design a self-supervised module with two data-augmentation strategies, namely historical behavior cropping and reordering, and optimize expert representations via contrastive learning over augmented sequences of historically answered questions. On the question view, we apply analogous augmentation strategies to capture intrinsic semantic differences among questions. The two view-specific modules are unified through multi-task learning with shared PLM parameters, enabling the model to capture latent semantic relatedness across views. Extensive experiments on six real-world CQA datasets demonstrate that SSEF consistently outperforms existing methods, and further analysis confirms its effectiveness under zero-shot settings and its transferability to other models.