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

Featured Application The technique proposed in this paper could be used to provide smarter recommendations via analyzing the semantics of the social information gathered from various sources in a socially connected and networked environment. Such applications could be performing recommendations on things such as products, restaurants, travel services, medical services, insurance service and so on to provide problem solutions or enable smart decisions or recommendations based on the gathered social information. Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.