  Accurate prediction of users' responses to items is one of the main aims of
many computational advising applications. Examples include recommending movies,
news articles, songs, jobs, clothes, books and so forth. Accurate prediction of
inactive users' responses still remains a challenging problem for many
applications. In this paper, we explore the linear mixed model in
recommendation system. The recommendation process is naturally modelled as the
mixed process between objective effects (fixed effects) and subjective effects
(random effects). The latent association between the subjective effects and the
users' responses can be mined through the restricted maximum likelihood method.
It turns out the linear mixed models can collaborate items' attributes and
users' characteristics naturally and effectively. While this model cannot
produce the most precisely individual level personalized recommendation, it is
relative fast and accurate for group (users)/class (items) recommendation.
Numerical examples on GroupLens benchmark problems are presented to show the
effectiveness of this method.
