Recently, personalized recommender systems have become indispensable in a wide variety of commercial applications due to the vast amount of overloaded information. Network-based recommendation algorithms for user-object link predictions have achieved significant developments. But most previous researches on network-based algorithm tend to ignore users’ explicit ratings for objects or only select users’ higher ratings which lead to the loss of information and even sparser data. With this understanding, we propose an improved network-based recommendation algorithm. In the process of reallocation of user’s recommendation power, this paper originally transfers users’ explicit scores to users’ interest similarity and user’s representativeness. Finally, we validate the proposed approach by performing large-scale random sub-sampling experiments on a widely used data set (Movielens) and compare our method with two other algorithms by two accuracy criteria. Results show that our approach significantly outperforms other algorithms.