New method for news recommendation based on Transformer and knowledge graph

被引：0

作者：

Feng L.-Z. ^{[1
]}

Yang Y. ^{[1
]}

Wang Y.-W. ^{[2
]}

Yang G.-J. ^{[1
]}

机构：

[1] School of Statistics, Tianjin University of Finance and Economics, Tianjin

[2] School of Information, Central University of Finance and Economics, Beijing

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 01期

关键词：

attention mechanism; high-order structural information; knowledge graph; news entity; news recommendation;

D O I：

10.3785/j.issn.1008-973X.2023.01.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A news recommendation method based on Transformer and knowledge graph was proposed to increase the auxiliary information and improve the prediction accuracy. The self-attention mechanism was used to obtain the connection between news words and news entities in order to combine news semantic information and entity information. The additive attention mechanism was employed to capture the influence of words and entities on news representation. Transformer was introduced to pick up the correlation information between clicked news of user and capture the change of user interest over time by considering the time-series characteristics of user preference for news. High-order structural information in knowledge graphs was used to fuse adjacent entities of the candidate news and enhance the integrity of the information contained in the candidate news embedding vector. The comparison experiments with five typical recommendation methods on two versions of the MIND news dataset show that the introduction of attention mechanism, Transformer and knowledge graph can improve the performance of the algorithm on news recommendation. © 2023 Zhejiang University. All rights reserved.

引用

页码：133 / 143

页数：10

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