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Recommender systems serves as an important technical asset in many moderncompanies. With the increasing demand for higher precision of the technology,more and more research and investment has been allocated to the field. Oneimportant sub-field of recommender systems that has been stagnating iscontext-aware recommender systems. Due to the difficulty of collecting inputdataset, the amount of research on context-aware recommender systems is muchless than other sub-fields of recommender systems. In this paper, we propose anew algorithm named PowerMat to tackle the context-aware recommendationproblem. We build our theory on matrix factorization and Zipf's law, and alsomore recent research work such as DotMat. We prove by experiments that ourmethod achieves superior results to the classic matrix factorization algorithmand other context-aware recommender systems such as MovieMat+. In addition, bytheoretical analysis, we show that our algorithm solves the cold-start problemfor context-aware recommendation.

PowerMat: context-aware recommender system without user item rating values that solves the cold-start problem