An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

Over the past few years, number of users social network services has been exponentially increasing and it is now a natural source data that can be used by recommendation systems to provide important humans analyzing applicable providing personalized information users. In this paper, we propose an technique enables smart recommendations based on two specific types analysis user behaviors, such as influence activity. The components measure activity are identified. accuracy verified using Yelp shows significantly promising results could create smarter systems.