A solution of missing value in collaborative filtering recommendation algorithm
Qin Jie, Lei Cao, Hui Peng · 2015
In order to solve the sparsity of User-Item scoring matrix in collaborative fIltering recommendation, proposed an algorithm that combining user's interests with item's quality to calculate ungraded items in matrix. By setting the weight of user and item, synthesized the value of missing value, which were used to replace the ungraded value in scoring matrix to calculate similarity. Experiment shows that the algorithm can improve the recommendation effect, and when the user's weight values 0.4, MAE reaches minimum, and recommendation quality reaches maximum.