Model-Based Location Recommender System Using Geotagged Photos On Instagram

Instagram is one of the popular social media services used by a variety of people around the world. It has a huge number of active users. The more users, the larger and the more different Instagram data are available. In this paper, we propose a Model-based location recommender system (MLRS), which creates a profile for each location and uses it to recommend locations, based on user interests. Since our analysis does not have an appropriate dataset to check, we use both Foursquare and Instagram to create our dataset. Next, we propose the Term-Frequency and Inverse Document Frequency(TF-IDF) method to rank extracted hashtags of selected Instagram locations based on Instagram image captions. This gives us the main idea of locations, based on 30 recent image captions hashtag posted. Then, we used FastText to classify hashtags of each location post. We evaluated our system with a large-scale real dataset collected from Instagram concerning precision, recall and the F-measure. Finally, the experimental results show that the highest result achieved when the FastText model tested with n=1 with an F-measure of 77.8%.