To solve the sparsity problems of recommender systems,a recommendation method is designed by means of the combination of the non-neighbouring sequential pattern mining algorithms and the item-based Collaborative Filtering(CF) recommendation algorithms.

Aiming at the problem of data sparsity for collaborative filtering,a novel rough set-based collaborative filtering algorithm is proposed.

With the in-depth study, however, appearing sparse data imaging, which traditional methods can not used to reconstruct a clear image.

This paper focuses on the sparse data interpolation which is based on Kriging method,and an improved method named Regression Kriging method is derivated.

The key problem in N-gram method is the problem of sparse data which still can not be solved effectively now.

Based on N-gram models, this paper proposes a three-step method of "word-similar word-part of speech" by incorporating the similar words and solves the problem of sparse data to a large extent.

Effective hybrid collaborative filtering algorithm for alleviating data sparsity

In order to solve the technological problems in the data sparsity and real time recommendation,a customer cooperation recommendation mechanism based on the interest degree vector model was proposed with integrating the traditional customer cooperation filtering technology.