Chongchao Cai1, 2, Huahu Xu1, *, Jie Wan2, Baiqing Zhou2, Xiongwei Xie3
CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2475-2488, 2020, DOI:10.32604/cmc.2020.011693
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… More >
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