Collaborative location recommendations with dynamic time periods

This study proposes a location-based collaborative filtering recommendation system with dynamic time periods (LCFDTPs) for recommending timely and suitable points of interest (POIs) to mobile users. The system expedites calculating similarity based on POI recency and enables mobile users to promptly obtain recommended items that closely match their current space-time conditions by selecting different strategies for dissimilar situations. The performance of the proposed system was evaluated through simulations. The simulation results revealed that compared with three existing strategies, the proposed LCFDTP system demonstrated higher recommendation accuracy and coverage and a shorter average recommendation time. When the users' moving velocity was set to 50 km/h and the query radius was set to 2 km, the recommendation precision of the proposed system was 61% higher than those of the other strategies. Moreover, the recommendation coverage and average response time of the LCFDTP system were 9% higher and 62% shorter than those of the other compared strategies, respectively. The LCFDTP system can also improve the POI recommendation quality by applying location-based services, thus enhancing user satisfaction.