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.
Highlights
Many social media websites, such as Instagram or Foursquare, serve as platforms to gather the data that their users share
Even after getting a complete list with the best touristic attractions on a city, many users may find it tedious to plot an itinerary by themselves including the many point of interests (POI) to visit
We will build up the groups of candidates for these POI from social media websites and extract the content from the photo metadata such as geolocation, tags, number of views, timestamp, and encrypted user ID
Summary
Many social media websites, such as Instagram or Foursquare, serve as platforms to gather the data that their users share. [6] developed a collaborative filtering system based on contextual information of geotagged photos Their framework considers 5 aspects: the user information, the geotags, the timestamps, the tags and visual information. Even after getting a complete list with the best touristic attractions on a city, many users may find it tedious to plot an itinerary by themselves including the many point of interests (POI) to visit. We exploited the geotagged photo dataset from social media websites With this information, we can transform the users’ travel log histories into trajectories and later use them to train a recommendation system to advise future travellers about the places they would be more likely to visit and arrange trips for them
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
