Friend Recommendation Algorithm for Online Social Networks Based on Location Preference

2016 ◽  
Author(s):  
Manfang Wu ◽  
Zhanquan Wang ◽  
Haoran Sun ◽  
HaiLong Hu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation Algorithm ◽  
Friend Recommendation

A privacy-preserving friend recommendation scheme in online social networks

2018 ◽  
Vol 38 ◽  
pp. 275-285 ◽  
Author(s):  
Shiwen Zhang ◽  
Xiong Li ◽  
Haowen Liu ◽  
Yaping Lin ◽  
Arun Kumar Sangaiah
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Preserving ◽  
Friend Recommendation

Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

2020 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Shudong Liu ◽  
Ke Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Slot ◽  
Empirical Studies ◽  
Real Life ◽  
Periodic Trajectories ◽  
The Public ◽  
Friend Recommendation ◽  
Location Based Social Networks ◽  
Friend Recommendations

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

scholarly journals Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

2019 ◽  
Vol 8 (9) ◽  
pp. 415
Author(s):  
Heba M. Wagih ◽  
Hoda M. O. Mokhtar ◽  
Samy S. Ghoniemy
Keyword(s):  
Social Networks ◽  
Peer Influence ◽  
Online Social Networks ◽  
Large Scale ◽  
Social Ties ◽  
Mutual Trust ◽  
Friend Recommendation ◽  
Influential Nodes ◽  
Location Based Social Networks ◽  
Virtual Interactions

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.

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