K-core decomposition in recommender systems improves accuracy of rating prediction

Users' ratings in recommender systems can be predicted by their historical data, item content, or preferences. In recent literature, scientists have used complex networks to model a user-user or an item-item network of the RS. Also, community detection methods can cluster users or items to improve the prediction accuracy further. However, the number of links in modeling a network is too large to do proper clustering, and community clustering is an NP-hard problem with high computation complexity. Thus, we combine fuzzy link importance and K-core decomposition in complex network models to provide more accurate rating predictions while reducing the computational complexity. The experimental results show that the proposed method can improve the prediction accuracy by 4.64% to 5.71% on the MovieLens data set and avoid solving NP-hard problems in community detection compared with existing methods. Our research reveals that the links in a modeled network can be reasonably managed by defining fuzzy link importance, and that the K-core decomposition can provide a simple clustering method with relatively low computation complexity.