Time-Slot-Based POI Recommendations Using User’s Current Location

With the expeditious development of the location-based social networking, people are able to share their information regarding places such as tips, experience, and check-in data. The category of location feature data is applied for location recommendation by taking into consideration of two factors: temporal and distance-based patterns. The temporal feature is represented as the periodic check-in behavior of different user’s at different locations. The similarity between users’ is predicted by leveraging the temporal influential model. A topographical influence scheme is proposed to exclude point-of-interest that does not match to the user preference. By infusing the time-based influence and the topographical patterns, a point-of-interest recommendation scheme is proposed. Experimental result shows that the scheme performs better than existing location recommendation algorithms.