  The Matrix Factorization models, sometimes called the latent factor models,
are a family of methods in the recommender system research area to (1) generate
the latent factors for the users and the items and (2) predict users' ratings
on items based on their latent factors. However, current Matrix Factorization
models presume that all the latent factors are equally weighted, which may not
always be a reasonable assumption in practice. In this paper, we propose a new
model, called Weighted-SVD, to integrate the linear regression model with the
SVD model such that each latent factor accompanies with a corresponding weight
parameter. This mechanism allows the latent factors have different weights to
influence the final ratings. The complexity of the Weighted-SVD model is
slightly larger than the SVD model but much smaller than the SVD++ model. We
compared the Weighted-SVD model with several latent factor models on five
public datasets based on the Root-Mean-Squared-Errors (RMSEs). The results show
that the Weighted-SVD model outperforms the baseline methods in all the
experimental datasets under almost all settings.
