Deep expertise and interest personalized transformer for expert finding

Most existing expert finding methods in Community Question Answering (CQA) websites typically determine an expert’s suitability for answering one question based on their past answered questions. However, experts’ interests evolve over time, and their abilities to address questions vary. Consequently, effectively capturing the diverse interests and expertise of experts from their historical records poses a challenge due to dynamic preferences and varying abilities. In this paper, we propose an expert finding framework, which aims to capture experts’ diverse expertise and temporal-aware interests from their past answered questions. Specifically, we encode the timestamp and vote score information of each question answered by the expert for modeling their interests and expertise. Then, we design a personalized transformer encoder to effectively learn the inherent representation based on the expert’s historical answering behaviors. We further design an additive attention-based interaction encoder to dynamically capture the relevance between a target question and an expert’s historical answered questions. We conduct experiments on six real-world CQA datasets from StackExchange, the largest of which contains 8921912 questions and 687213 answerers. Experimental results show that on the metric P@1, compared with the best baseline methods, our method has achieved 3.3%–15.6% performance improvement.