Recommendation Algorithm Based on Multi-Label Clustering and Core Users

With the rapid growth of user scale, it is so meaningful to explore the users who carry more valuable information in the user group. The importance of the individual users in the recommender system can improve the recommendation efficiency of the recommender system, enhance the robustness of the recommender system, but there is little research work in this path and can not determine a more effective method. To solve this problem, we propose a method based on multi-label clustering to determine core user, and define the concept of correlation between user and label cluster, user location weight, considered the potential relationship between users and labels. At the same time, we proposed a new method based on multi-label clustering and core user, according to experiments demonstrate the effectiveness of the proposed algorithm, we have a more significant upgrade in the recommendation accuracy and diversity.