Expert Recommendation Algorithm Based on the Fusion of Transformer and CNN: Deep Semantic Modeling Combined with Multi-Task Learning
This paper proposes an expert recommendation algorithm based on the fusion of Transformer and Convolutional Neural Network (CNN) architectures, combined with a multi-task learning (MTL) framework, to improve the accuracy of expert recommendations in online question-answering communities. With the rapid development of online knowledge-sharing platforms, accurately matching user questions with suitable experts has become a key challenge for intelligent recommendation systems. Traditional recommendation methods often rely on shallow semantic features, making it difficult to capture the complex relationships between questions and expert knowledge. To overcome this limitation, the proposed model integrates the advantages of Transformer and CNN architectures, achieving comprehensive semantic modeling. Specifically, the Transformer module captures long-range dependencies and global contextual information through a self-attention mechanism, while the CNN module extracts important local features from textual data. By jointly modeling the deep semantics of expert historical answers and question text, the model learns richer text representations and achieves simultaneous optimization of expert recommendation and question classification tasks within a unified multi-task learning framework. Experimental results show that the proposed algorithm outperforms several baseline methods in terms of accuracy, AUC, and convergence speed. The results of this study provide a practical and effective solution for expert recommendation systems and highlight the potential of combining deep semantic modeling with multi-task learning to improve the performance of intelligent recommendation systems in practical applications.