Abstract：Point of interest (POI) recommendation has become an increasingly important sub-field of recommendation system and aims to find new places for users that they might be interested in. It can help users find interesting spots that will make them enjoy their vacations when they are in unfamiliar regions. And it can also increase the shopkeepers’ income by attracting more customers who would like to spend time and money at the store. Therefore, POI recommendation has become a hot research topic in recent years. However, there are many challenges in this problem and one of the most challenging one is the data sparsity problem. To tackle this problem, many methods incorporate the contextual information into the recommendation method with different assumptions. For example, some work assumes that the user will visit new POIs that are close to the POIs they visited before. And they construct an auxiliary label matrix by adding the weighted sum of neighboring POIs’ ratings to every POI. Some study assumes that users will have different preference patterns in different time slots, so they construct different models for different time intervals. Although the assumptions are various, the common property behind them is that similar users should visit similar POIs and similar POIs should be visited by similar users. Therefore, inn this seminar, how to utilize contextual information in the POI recommendation will be introduced with different frameworks by constructing similarity graphs.