Abstract
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.
Highlights
We propose an information recommendation technique that reflects two specific types of analysis based on the social relations among the users participating in a social network
We investigate the core components that can define the concepts of user influence and user activity based on social network data
We suggest that the users showing high user influence or high user activity can provide a rating that would be acceptable to most users
Summary
The explosive growth in social network services has made it a common place for communication among various communities and a platform to build relationships through interactions. With social network services becoming available, information recommendation approaches tried to use the social network information to solve the cold start problem by using neighbors of users in the social network to identify users with similar taste [5]. This may work in some situations, we find that. Formulas including these core components, to calculate the user influence and user activity, are devised Based on these concepts, we can map these core components to real individual social network data components.
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