Modeling user preferences on spatiotemporal topics for point-of-interest recommendation

With the development of the location-based social networks (LBSNs) and the popular of mobile devices, a plenty of user's check-in data accumulated enough to enable personalized Point-of-Interest recommendations services. In this paper, we propose a scheme of modeling user's preferences on spatiotemporal topics (UPOST scheme) for accurate individualized POI recommendation. In the UPOST scheme, we discover temporal topics from semantic locations (i.e., people's description words for a location) to learn users' preferences. UPOST infers user's preference for different types of places during different periods by learning the spatiotemporal topics from the historical semantic locations of users. We have developed two algorithms under the UPOST scheme: the time division LDA algorithm (TDLDA) and the time adaptive topic discovery algorithm (TATD). In TDLDA, we divide the check-in dataset into different time segments and use one LDA for one segment. Then we improve TDLDA further by developing a new TATD algorithm to discover spatiotemporal topics. The experimental results demonstrate the effectiveness of our UPOST scheme, both TDLDA and TATD outperform the counterpart method that do not consider semantic locations