User Preferences-Based and Time-Sensitive Location Recommendation Using Check-In Data

Location-based social networks have attracted increasing users in recent years. Human movements and mobility patterns have a high degree of freedom and provide us with a lot of trajectory to understand the activity of users. In this paper, we present a user preferences and time sensitive recommender systems that offer an appropriate venue for a user when he appears in a special time at a particular location. The system considering the factors are: 1) the popularity of a location; 2) the preferences of a user; 3) social influence of the friends of the user and the friends who are check-in at the same location with the user; and 4) the time feature of the location and the user visiting. We evaluate our system with a large-scale real dataset from a location-based social network of Gowalla. The results confirm that our method provides more accurate location recommendations compared to the baseline.