A Random Walking Recommendation Algorithm Based on Conditional Restricted Boltzmann Machine in Trust Network

Abstract

In order to improve the accuracy of the recommendation system and solve the data sparsity problem, this paper proposes a random walking recommendation algorithm based on conditional restricted Boltzmann machine in trust network, namely CRBM_PrTW. The algorithm fills the missing data in training data by utilizing the conditional restricted Boltzmann machine to improve the accuracy of similarity calculation, which effectively solves data sparsity problem. And on this basis, the comprehensive weight of the credibility, similarity and the trust value utilized as trust level has been implemented to random walking algorithm. The experimental result on the Epinions dataset demonstrates that our method can provide better recommendation result in terms of evaluation metrics when compared with the existing methods.