A Fast Similarity Calculation Method Based on Cotangent Similarity and BP Neural Network

Abstract:

Similarity measurement is of great significance in big data related applications. However， the traditional cosine similarity traversal calculation method has a poor accuracy and timeliness， which cannot provide an effective basis for the quality assessment of massive high-dimensional data. To improve the accuracy of similarity calculation， two types of cotangent similarity formulas with cotangent trigonometric function and data dimensional differences was constructed. Besides， a back-propagation（BP） neural network model approximating the similarity mapping relationship of datasets was established to reduce the time complexity. The experimental results demonstrate that the improved fast similarity calculation method has a good accuracy and timeliness. Moreover， it has a more significant performance improvement when applied to large-scale datasets.