Online travel destination recommendation with efficient variable memory Markov model

Online travel destination recommendation is to keep track of a user´s current traveling history to recommend next destination in real time while the user is on the travel. This paper presents an efficient variable memory Markov model based method to provide such recommendation. The proposed method utilizes the large quantity of geotags from photo sharing website and combines travel pattern, location´s popularity and distance factors to generate real time recommendation. Experiments on Panoramio data set demonstrate the effectiveness of this method.