  Understanding user preference is essential to the optimization of recommender
systems. As a feedback of user's taste, rating scores can directly reflect the
preference of a given user to a given product. Uncovering the latent components
of user ratings is thus of significant importance for learning user interests.
In this paper, a new recommendation approach, called LCR, was proposed by
investigating the latent components of user ratings. The basic idea is to
decompose an existing rating into several components via a cost-sensitive
learning strategy. Specifically, each rating is assigned to several latent
factor models and each model is updated according to its predictive errors.
Afterwards, these accumulated predictive errors of models are utilized to
decompose a rating into several components, each of which is treated as an
independent part to retrain the latent factor models. Finally, all latent
factor models are combined linearly to estimate predictive ratings for users.
In contrast to existing methods, LCR provides an intuitive preference modeling
strategy via multiple component analysis at an individual perspective.
Meanwhile, it is verified by the experimental results on several benchmark
datasets that the proposed method is superior to the state-of-art methods in
terms of recommendation accuracy.
