  Recommender systems serves as an important technical asset in many modern
companies. With the increasing demand for higher precision of the technology,
more and more research and investment has been allocated to the field. One
important sub-field of recommender systems that has been stagnating is
context-aware recommender systems. Due to the difficulty of collecting input
dataset, the amount of research on context-aware recommender systems is much
less than other sub-fields of recommender systems. In this paper, we propose a
new algorithm named PowerMat to tackle the context-aware recommendation
problem. We build our theory on matrix factorization and Zipf's law, and also
more recent research work such as DotMat. We prove by experiments that our
method achieves superior results to the classic matrix factorization algorithm
and other context-aware recommender systems such as MovieMat+. In addition, by
theoretical analysis, we show that our algorithm solves the cold-start problem
for context-aware recommendation.
