Multi-hop interactive attention based classification network for expert recommendation

• Published in: Neurocomputing (Amsterdam) Vol. 488; pp. 436 - 443
• Main Authors: Qian, Lingfei, Wang, Jian, Lin, Hongfei, Yang, Liang, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022
• Subjects: Attention mechanism; Community question answering; Expert recommendation; Neural network; Attention mechanism; Community question answering; Neural network; Expert recommendation
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.02.033

Community question answering (CQA) is a popular platform where users can ask questions or solve the questions proposed by other users. The expert recommendation aims at providing high-quality answers for the newly proposed questions in time, which is the key to a successful CQA. Questions in CQA usually consist of two parts, a subject which describes the main point, and a body which gives the details of the question. In previous studies, researchers usually ignore the differences between the subject and the body and concatenate them as a whole. In this paper, we propose a multi-hop interactive attention based classification network (MIACN) to recommend experts for newly proposed questions. In our model, the subject and the body are seen as two separate parts. A multi-hop attention is used to capture the multiple latent interactions among the two parts. Then, a high-level representation of the question is generated from the interactions. Experiment results on two real-world datasets demonstrate the effectiveness of our model.