News Recommendation Model Based on Transformer and Heterogenous Graph Neural Network
ZHANG Yupeng1, LI Xiangju1, LI Chao2, ZHAO Zhongying1
1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590; 2. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590
Abstract：In most of the existing news recommendation models, it is assumed that there is strong temporal dependence among the news items browsed by users. However, noise may be introduced into temporal modeling due to the rapidity of news updates and the freedom for users to read. To solve the problem, a news recommendation model based on Transformer and heterogenous graph neural network is proposed. Different from the neural network model based on time series, Transformer is employed to model the users’ short-term interests from the recent reading history. Using heterogenous graph neural networks, users’ long-term interests and candidate news representations are modeled by capturing the high-order relationship information between users and news. Meanwhile, a long and short-term interests aware mechanism is designed to adaptively adjust the importance of users’ long-term and short-term interests in news recommendation. Experiments on a real-world dataset demonstrate the effectiveness of the proposed model.
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2022, Vol. 35 
