Check-in prediction is an important task for location-based systems, which maps a noisy estimate of a user's current location to a semantically meaningful point-of-interest (POI), such as a restaurant or store. In this paper, we leverage the personalized preference and sequential check-in pattern to improve the traditional methods that base on the geographical and temporal contexts. In our approach, we propose a Gaussian mixture model and a histogram distribution estimation model to learn the contextual features from relevant spatial and temporal information, respectively. Furthermore, we employ user and POI embeddings to model the personalized preference and leverage a stacked Long-Short Term Memory (LSTM) model to learn the sequential check-in pattern. Combining the contextual features and the personalized sequential patterns together, we propose a wide and deep neural network for the check-in prediction task. Experimental evaluations on two real-life datasets demonstrate that our proposed method outperforms state-of-the-art models.