Personalized Recommendation of Tourist Attractions based on LBSN

Photos metadata in Location-Based Social Networks (LBSN) contain rich time and space information, these metadata provide the basis for the research of personalized recommendation of tourist attractions. The existing methods have many problems such as low accuracy of recommendation and single type of attractions recommendation. For those problems, the PRTA-CF algorithm is proposed to improve the method of predicting the user’s preference of attractions that he/she has not been to before, which is used in traditional collaborative filtering. We designed an evaluation model to assess the user’s preference of the attractions he/she has been to. In order to predict the target user’s preference to attractions he/she has not been to before, when we recommended to the target user, we took into account similar users’ recommendation value and the popularity of attractions based on user preference. Experiment shows that compared with the traditional collaborative filtering algorithm and the algorithm only considered similar users’ preference, it can effectively improve the accuracy of personalized recommendation of tourist attractions when considering both.