Affiliations: [a] School of Software, Xin Jiang 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. Yurong Qian, School of Software, XinJiang University, Urumqi, 830000, China. E-mail: qyr@xju.edu.cn.
Abstract: In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model.
Keywords: User profile, bert, canopy, K-means, text classification
