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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