@Article{cmc.2020.011693,
AUTHOR = {Chongchao Cai, Huahu Xu, Jie Wan, Baiqing Zhou, Xiongwei Xie},
TITLE = {An Attention-Based Friend Recommendation Model in Social  Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2475--2488},
URL = {http://www.techscience.com/cmc/v65n3/40182},
ISSN = {1546-2226},
ABSTRACT = {In social networks, user attention affects the user’s decision-making, resulting 
in a performance alteration of the recommendation systems. Existing systems make 
recommendations mainly according to users’ preferences with a particular focus on items. 
However, the significance of users’ attention and the difference in the influence of 
different users and items are often ignored. Thus, this paper proposes an attention-based 
multi-layer friend recommendation model to mitigate information overload in social 
networks. We first constructed the basic user and item matrix via convolutional neural 
networks (CNN). Then, we obtained user preferences by using the relationships between 
users and items, which were later inputted into our model to learn the preferences 
between friends. The error performance of the proposed method was compared with the 
traditional solutions based on collaborative filtering. A comprehensive performance 
evaluation was also conducted using large-scale real-world datasets collected from three 
popular location-based social networks. The experimental results revealed that our 
proposal outperforms the traditional methods in terms of recommendation performance.},
DOI = {10.32604/cmc.2020.011693}
}