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Point of interest recommendation is a critical issue in location based social networks. Traditional recommender algorithms used either naive collaborative filter algorithms, or space feature based collaborative filter algorithms. However, these algorithms neglected the importance of time feature in point of interest recommendation. In order to improve the performance of algorithms in point of interest recommendation, this paper proposed a time feature based point of interest recommender algorithm in location based social networks. Firstly, it described the naive user based collaborative filter algorithm. Secondly, it analyzed the importance of time and space features separately, and proposed corresponding models. Finally, it fused the time and space features, and proposed a unified recommender algorithm. The experiments show that, the proposed algorithm has better precision and recall compared with related works, and thus can be used in real point of interest recommender services. [ABSTRACT FROM AUTHOR]