Vol.65, No.3, 2020, pp.2475-2488, doi:10.32604/cmc.2020.011693
OPEN ACCESS
ARTICLE
An Attention-Based Friend Recommendation Model in Social Network
  • Chongchao Cai1, 2, Huahu Xu1, *, Jie Wan2, Baiqing Zhou2, Xiongwei Xie3
1 School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
2 College of Logistic and Information Engineering, Huzhou Vocational & Technical College, Huzhou, 313000, China.
3 Google, New York, USA.
* Corresponding Author: Huahu Xu. Email: xuhuahu123@outlook.com.
Received 24 May 2020; Accepted 28 July 2020; Issue published 16 September 2020
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.
Keywords
Friend recommendation, collaborative filtering, attention mechanism, deep learning.
Cite This Article
Cai, C., Xu, H., Wan, J., Zhou, B., Xie, X. (2020). An Attention-Based Friend Recommendation Model in Social Network. CMC-Computers, Materials & Continua, 65(3), 2475–2488.
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