Progressive Recommendation by Incremental Tensor Factorization

There are several circumstances in which constantly updated multidimensional tensor data must be analyzed in real-time in order to yield quick recommendation in our fast-changing data world. Methods for incremental tensor decompositions are powerful tools for analyzing and predicting fast-growing multidimensional datasets. In this research, we provide a robust model that overcomes the limitations of the most popular incremental tensor decomposition methods and yields high-accuracy prediction results for enormous datasets with fast execution time. Testing our model on datasets, we discovered that it was able to create a tensor summary that could reflect both the new and old datasets properly and performed better than traditional static methods.