Research on dual-channel user portrait construction method based on DPCNN-BiGRU and attention mechanism

Article type: Research Article

Authors: Leng, Hongyong^{a; b; *} | Shao, Jinxin^a | Zhang, Zhe^a | Qian, Yurong^a | Ma, Mengnan^a | Li, Zichen^c

Affiliations: [a] School of Software, XinJiang University, Urumqi, China | [b] School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China | [c] Big Data and Artificial Intelligence Academy, Guangdong Water Conservancy and Electricity Vocational and Technical College, Guangzhou, China

Correspondence: [*] Corresponding author. Hongyong Leng, E-mail: leng@xju.edu.cn.

Abstract: To address the problem that single-channel neural networks cannot fully extract text semantic features in traditional user portrait construction methods, this paper proposes a dual-channel user portrait model based on DPCNN-BIGRU and attention mechanism. The model first uses Bidirectional Encoder Representation from Transformers(Bert) and CK-means+ to obtain the fusion vector of semantic features and topic features, and then feeds the vector into Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Gated Recurrent Unit (BiGRU). Deep features and global features of the text are obtained simultaneously, and then weights are assigned by the attention mechanism. Finally, the output features of the dual channels are fused and classified. It is tested on the Sogou user portrait datasets, and the experimental results prove that the dual-channel model outperforms the baseline model.

Keywords: User profile, BERT, canopy, K-means, text classification

DOI: 10.3233/JIFS-224532

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2579-2591, 2023