Abstract
The matrix decomposition is one of the most powerful methods in recommendation systems. In the recommendation system, even if evaluation values in a matrix where users and items are corresponding to row and column are provided incompletely, we can predict the vacant elements of the matrix using the observed values. This method is applied to a variety of the fields, e.g., for movie recommendations, music recommendations, book recommendations, etc. In this paper, we have applied the matrix decomposition method to predict the seasonal infectious disease spread prediction such as streptococcal pharingitis cases, influenza, and etc., and we have found that the proposed method has an ability for prediction equivalent to the traditional time series analysis such as ARIMA when the seasonality is apparently shown.
