多视角提供数字产品预测可靠性的推荐策略研究——基于直觉模糊集的自然噪声检测机制

Predicting Reliability of Digital Products from Multiple Perspectives Based on Intuitionistic Fuzzy Sets Natural Noise Detection Mechanism

【目的】考虑原始评分信息的准确性及其预测结果的可靠性,以提升推荐系统的准确性。【方法】从信息输入和输出两方面,设计三种方案为已有推荐算法的预测结果提供可靠性概率。在信息输入方面,借助直觉模糊集理论,提出模糊自然噪声检测机制识别和修正有误评分;在信息输出方面,分别采用二次模糊噪声检测、矩阵分解和深度神经网络获得待预测位置的可靠性概率,并根据设定的可靠性判别条件,识别出不可信的预测评分并对其修正。【结果】在两个公开数据集上的实验结果显示,与原始推荐算法相比,引入所提模糊自然噪声检测方法和三种可靠性方案后的相应方法在F1值和NDCG评估指标上分别最高提升了6.4%和7.2%。【局限】所设计的可信推荐策略不适用于只包含隐式反馈的数据集。【结论】从评估信息可靠性的视角,为提升推荐算法的性能提供了新的解决方案。

[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.