With the increasing number of TV channels, it is more difficult for viewers to find their preferred TV channel. Thus, the recommender system for TV is needed. However, it has several difficulties. First, the viewer's preferred TV channel is different according to the temporal context. Moreover, the sparseness problem also occurs when we consider temporal context. Temporal context has been recognized as an important factor to consider in personalized recommender systems. A lot of time aware recommendation methods were proposed for these difficulties. In this paper, we survey and compare some techniques for time aware TV channel recommendation such as Singular Value Decomposition (SVD), traditional Matrix Factorization (MF), and Temporal Regularized Matrix Factorization (TRMF). We apply them for real-world data to analyze possible benefits of temporal context information for TV channel recommendation and compare the performance of each of them.