Fig. 3
From: Recommendation algorithm based on user score probability and project type
Recommendation algorithm model of integrating score preference and project type. On the basis of the conventional algorithm based on neighborhood, the ideas of the user behavior in Fig. 1 and the improved similarity of Fig. 2 are integrated. The improved algorithm framework of this chapter is obtained, which is called the recommendation algorithm framework of scoring preference and project type. The core idea of the model of score preference and project types in Fig. 3: Firstly, we calculate the similarity S1 based on the matrix. Then, we calculate the possibility Pro of the user to score the product according to preference information implied in the user rating behavior. The second similarity S2 is calculated according to Pro and the type of the product. Due to the different weights of the two similarities, the final similarity of the user S is obtained by combining the two similarities S1 and S2