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Multi-hop interactive attention based classification network for expert recommendation
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AI
DOI：10.1016/j.neucom.2022.02.033.png)
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Abstract
En 中文
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. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Community question answering
Expert recommendation
Neural network
Attention mechanism