A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks

Abstract

In social network recommendation systems, the rating score prediction accuracy of the collaborative filtering (CF) method depends on both the extraction of the nearest neighbors and the calculation of user/project similarity. Based on a similar principle to user/project behavior, this paper uses the maximum intersection method to extract the optimal neighbor candidate set, and presents a weighted adjusted cosine similarity method to compute user/project similarity. Furthermore, to optimize the weights of the method, a novel optimization method called the discrete quantum-inspired shuffled frog leaping (DQSFL) algorithm is proposed, which is based on the shuffled frog leaping algorithm and quantum information theory. The DQSFL algorithm uses quantum movement equations to search for the optimal location according to the co-evolution of the quantum frog colony. The experiments demonstrate that the CF recommendation method based on DQSFL can effectively solve the rating data sparseness problem in the similarity computation process to improve the accuracy of the rating score prediction, and provide a better recommended result than traditional CF algorithms.