Predicting Reliability of Digital Products from Multiple Perspectives Based on Intuitionistic Fuzzy Sets Natural Noise Detection Mechanism
[Objective]This paper aims to enhance the accuracy of the recommendation system,considering the accuracy of the original rating information and the reliability of its prediction results.[Methods]First,we designed three schemes to provide the reliability probabilities for the prediction results of existing methods from the perspectives of information input and output.For information input,we proposed a fuzzy natural noise detection mechanism based on intuitionistic fuzzy set theory to identify and correct erroneous ratings.For information output,we adopted quadratic fuzzy noise detection,matrix factorization,and deep neural networks to obtain the reliability probabilities of predicted positions.Finally,we identified and corrected the unreliable prediction ratings based on the set reliability discrimination criteria.[Results]We examined the new method with experiments on two public datasets.Compared with the original recommendation methods,the new model achieved the highest improvements of 6.4％and 7.2％in F1 value and NDCG evaluation metrics.[Limitations]Our strategy does not apply to datasets containing only implicit feedback.[Conclusions]This paper provides a new solution for improving the performance of recommendation algorithms by measuring information reliability.
Intuitionistic Fuzzy SetNatural NoiseReliabilityMachine LearningRecommender System
NETL
NSTL
万方数据
