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Research on Different Weights of Single-Valued Neutrosophic Sets in Recommendation System

The objective of this paper is to explore the influence of different weights about true value membership degree, uncertain value membership degree and non-true value membership degree on the error of recommendation algorithm. Firstly, the membership degrees of single-valued neutrosophic set (SVNS) corresponding to the rating are obtained utilizing the rating conversion formula of SVNS. Then, a new similarity of SVNS is constructed. Lastly, the weights of true value membership degree, uncertain value membership degree and non-true value membership degree are strengthened respectively, and the experimental analysis is carried out based on public data set. Experimental results show that among the three membership degrees, the true value membership degree has a great impact on the error of the recommendation algorithm, that is, enhancing the weight of true value membership degree can improve the accuracy of the recommendation algorithm by 2–3 percent.