Personalized Tour Recommender through Geotagged Photo Mining and LSTM Neural Networks

Abstract

In this study, a tour recommendation system based on social media photos is proposed. The proposed recommendation system can generate trip tours considering both the user’s current location and interests. First, we exploited the geotagged photo dataset from social media websites, which includes photo related information such as user ID numbers, timestamps, hashtags, and GPS coordinates. With this information, the second step is to group photos and identify those places that could be considered relevant for travellers using clustering algorithms. The third step characterizes the resulting clusters by grouping them into different categories using latent dirichlet allocation (LDA) topic modelling approach. The last step is the generation of tours using a long-short term memory neural network (LSTM). The experiments show that the proposed system can be efficient to advise future travellers about the places they would be more likely to visit and arrange trips for them.