Bert-CK: A study of user profile classification based on Bert and CK-means+ fusion

Author:

Qian Yurong1,Shao Jinxin1,Zhang Zhe1,Leng Hongyong12,Ma Mengnan1,Li Zichen3

Affiliation:

1. School of Software, Xin Jiang University, Urumqi, China

2. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

3. Big Data and Artificial Intelligence Academy, Guangdong Water Conservancy and Electricity Vocational and Technical College, Guangzhou, China

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.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference16 articles.

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