A reinforced collaborative filtering approach based on similarity propagation and score predication graph

Abstract

In the era of big data, the rapid development of mobile participatory sensing devices brings the explosive expansion of data, making information overload a serious problem. In this case, a personalized recommendation system on mobile social media appears. Collaborative filtering is the most widely used approach in a recommendation system. Nevertheless, there still exist many problems, such as the serious data sparsity problem and the cold start problem. Existing approaches cannot effectively solve these problems. Most of the existing recommendation approaches are based on single information source and cannot effectively solve the cold start and data sparsity problems. In addition, some approaches proposed to solve data sparsity fail to consider the effects of users' influences and prediction order on recommendation accuracy. Accordingly, from the perspective of increasing the categories of information, the similarity propagation approach based on a heterogeneous network is proposed to ease the cold start problems by improving the similarity calculation method. In addition, to ease the data sparsity problems, we propose a hybrid collaborative filtering approach based on a score prediction graph to finish the user-item score matrix in order. Finally, we conduct validation experiments on the MovieLens dataset. Compared with five state-of-the-art approaches, our approach outperforms them in terms of the performances of mean absolute error, root-mean-square error, recall, and diversity.