A fast way for finding similar friends in social networks by using neuro-fuzzy networks

The key function of recommendation systems is to analyze social network data and identify similar users. In recent years, multi-criteria algorithms called skyline queries have been used in conjunction with neural networks to search for similar users. However, many skyline query methods are slow and...

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Bibliographic Details
Main Authors: Sheng-Min Chiu, Yi-Chung Chen, Tien-Yen Chang, Yu-Liang Hsu, Heng-Yi Su, Hsi-Min Chen, Tzu-Yu Lin
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Cybernetics
Eigenvalues and eigenfunctions
Fuzzy neural networks
Mathematical model
Neural networks
Neuro-fuzzy networks
Recommendation systems
Skyline queries
Social network services
Social networks
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Summary:The key function of recommendation systems is to analyze social network data and identify similar users. In recent years, multi-criteria algorithms called skyline queries have been used in conjunction with neural networks to search for similar users. However, many skyline query methods are slow and cumbersome, and generate user groups that are not similar enough to the target user. To solve these problems, we built an approximate skyline region using a neuro-fuzzy network. Simulation results demonstrated the effectiveness and efficiency of our approach.
ISSN:2160-1348
DOI:10.1109/ICMLC.2016.7872945