In the CLEF NewsREEL 2017 challenge, we build a delegation model based on the contextual bandit algorithm. Our goal is to investigate whether a bandit approach combined with context extracted from the user side, from the item side and from user-item interaction can help choose the appropriate recommender from a recommender algorithm pool for the incoming recommendation requests. We took part in both tasks: NewsREEL Live and NewsREEL Replay. In the experiment, we test several bandit approaches with two types of context features. The result from NewsREEL Replay suggests that delegation model based on the contextual bandit algorithm can improve the click through rate (CTR). In NewsREEL Live, a similar delegation model is implemented. However, the delegation model from NewsREEL Live is trained by the data stream from NewsREEL Replay. This is due to the fact that the low volume of data received from the online scenario is not enough to support the training of the delegation model. For our future work, we will add more recommender algorithms to the recommender algorithm pool and explores other context features.