The Computing of Optimized Clustering Threshold Values Based on Quasi-Classes Space for the Merchandise Recommendation

Abstract

The merchandise recommendation is an important part of electronic commerce. In view of the difficulty in obtaining user private information and modeling user interest, this paper is based on the relationship between goods for commodity recommendation. We use fuzzy clustering learning to construct quasi-classes space. Through the intersection of quasi-class and the collection of goods that are being ordered by users, we can know the customers appetites for merchandise, and then recommend the goods. In the construction of quasi-classes space, the value of the threshold Λ must be appropriate, because the threshold Λ determines the size of the quasi-class. It will affect the recommendation of the goods that the size of the quasi-class is too large or too small. The influence of threshold Λ on commodity recommendation is discussed by numerical example, and we finally find the best value of Λ in this paper.