GPS-Based Personalized Point-of-Interest Recommendation Algorithm

The popularity of GPS makes it possible to record people’s moving trajectories which contain the users' interests and preferences. Different moving trajectories reflect different interests of the users. In view of the low precision of the traditional tourism recommendation algorithm in tourist spot recommendation, the paper puts forward a personalized tourist spot recommendation algorithm based on GPS and LBSN. The algorithm can acquire the users' tourism intentions according to their position and time information and establish the user preference model based on the information, so as to generate multiple tourist spots for them to choose in real time and recommend them the appropriate ones. The experimental results on the real data set show that compared with the existing similar algorithms, the GPSRec algorithm in the paper has higher recommendation precision.