It is a valuable study for Location-based Social Network (LBSN) make a moreaccurate Points-of-Interest (POI) recommendation since that can improve users'experiences. There have been many methods of POIs recommendation that consider context, personal preference pattern, and/or matrix factorization. However, the continuouscontexts have not been thoroughly considered in these methods. This paper first proposes a locations splitting method which can handle both continuous and discrete contexts. Moreover, we present a Context-aware Probabilistic Matrix Factorization method (CPMF)that factorizes a frequency matrix of contexts and locations to obtainthe user-location checkin probabilities. We design a Personal Preference Confidence (PPC) toextract a set of reliable POIs with confidence values for every user. Finally, we propose a hybrid recommender which fuses CPMF with PPC to recommend top-n POIs. Experiments on a large-scale real-worldcheckins dataset demonstrate that our recommendation method obtains a well performance and effect.