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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...
This paper presents a relational compound collaborative filtering (RCCF) recommendation system architecture, which integrated the behaviors associated mechanism and recommendation system to calculate the area associated values and the corresponding region of the recommended items rating, resulting in top-N list of the recommended item values. This system can avoid interested items in the MU¡¦s opposite...
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