An Approach for Opinion-Demographic-Topology Based Microblog Friend Recommendation

Advances in Intelligent Systems and Computing - Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 ◽

10.1007/978-3-319-48308-5_78 ◽

2016 ◽

pp. 815-824

Author(s):

Sherin Moussa

Keyword(s):

Friend Recommendation

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Friend or Foe: Studying user Trustworthiness for Friend Recommendation in the Era of Misinformation

2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) ◽

10.1109/aike.2019.00053 ◽

2019 ◽

Cited By ~ 1

Author(s):

Antonela Tommasel

Keyword(s):

Friend Recommendation

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A Privacy-Preserving Friend Recommendation Mechanism for Online Social Networks

Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy ◽

10.1145/3377644.3377648 ◽

2020 ◽

Author(s):

Fukang Liu ◽

Guorui Wu ◽

Yang Liu

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Privacy Preserving ◽

Friend Recommendation

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PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

Mathematical Problems in Engineering ◽

10.1155/2016/8575187 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Wei Jiang ◽

Ruijin Wang ◽

Zhiyuan Xu ◽

Yaodong Huang ◽

Shuo Chang ◽

...

Keyword(s):

Social Network ◽

Recommendation System ◽

User Behavior ◽

Network Services ◽

Mobile Social Network ◽

Fine Grained ◽

Friend Recommendation ◽

Social Applications ◽

Browsing Behavior ◽

Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

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