Modeling Personalized Recommendations of Unvisited Tourist Places Using Genetic Algorithms

Immense amount of data containing information about preferences of users can be shared with the help of WWW and mobile devices. The pervasiveness of location acquisition technologies like Global Positioning System (GPS) has enabled the convenient logging of movement histories of users. GPS logs are good source to extract information about user’s preferences and interests. In this paper, we first aim to discover and learn individual user’s preferences for various locations they have visited in the past by analyzing and mining the user’s GPS logs. We have used the GPS trajectory dataset of 178 users collected by Microsoft Research Asia’s GeoLife project collected in a period of over four years. These preferences are further used to predict individual’s interest in an unvisited location. We have proposed a novel approach based on Genetic Algorithm (GA) to model the interest of user for unvisited location. The two approaches have been implemented using Java and MATLAB and the results are compared for evaluation. The recommendation results of proposed approach are comparable with matrix factorization based approach and shows improvement of 4.1 % (approx.) on average root mean squared error (RMSE).