Robust weighted SVD-type latent factor models for rating prediction

Recommending system is a popular tool in many commercial or social platforms which finds interesting products for users based on their preference history. Predicting the ratings of items, such as movies, plays an essential role in the recommending system. In this context, we develop a new type of latent factor models by attaching weights to the entries of the incomplete ratings matrix. The weights are computed after estimating the user/item mean errors caused by the basic SVD model under the low-rank assumption on the ratings matrix. To accelerate the optimization process of our proposed models and other existing SVD-type models, a special design of the initial guess is suggested. In the experiments on real-world datasets, the proposed weighted models outperform other SVD-type methods, and the usage of the special initial guess improves the optimization significantly, obtaining lower MRSEs within fixed number of iterations, in comparison with the random initial guess. Furthermore, artificially noised datasets are taken to evaluate the methods, where the weighted models still perform better than other SVD-type models, implying their effectiveness and robustness in noised environment.