Alleviate sparsity problem using hybrid model based on spectral co-clustering and tensor factorization

Most of recommender systems suffer from common challenge named data sparsity. This problem makes an important challenge on the efficiency of collaborative filtering and causes overfitting problem. In this paper, `time´ as a third dimension is considered. Therefore, increasing dimensions of data makes this problem more critical. For alleviating this problem, this research applies a framework to block users and items co-occurrences in the similar cluster and adds time to each block. This method imputes appropriate values for missing data based on similar user and item ratings assigned to each block. Preprocess based on optimization is performed on each cluster. After this step, data points in each block are merged together and tensor factorization is used to model relations between users, items and times. Our novel approach has two advantages: (a) it increases the speed of convergence and avoids overfitting the observed data, (b) it reduces sparsity problem and error rate. The evaluation metric demonstrate that our algorithm has good results in practice.