Impact of the Important Users on Social Recommendation System

Recommendation methods have attracted extensive attention recently because they intent to alleviate the information overload problem. Among them, the social recommendation methods have become one of the popular research fields because they are benefit to solve the cold start problem. In social recommendation systems, some users are of great significance, because they usually have decisive impacts on the recommendation results. However, it is still lack of research on how the important users make influence to recommendation methods. This paper presents three types of important users and utilizes three social frequently-used recommendation methods to analyze the influence from multiple perspectives. The experiments demonstrate that all the recommendation methods achieve better performance with important users, and the important neighbor users have the greatest impact on the recommendation methods.