Smart recommendations for tourists

1. CultwaysCULtural Tourism WAysthrough mobile Applications and Services Smart recommendationsfor tourists Ago Luberg ago.luberg@eliko.eeEliko Competence Centre, Estonia 16.10.2013 Rovereto, Italy

2. Outline • Recommendation engine in general • Example of Visit Estonia planner • Organization • World-wide recommender Sightsplanner • Automatic process • Main challenges

3. Recommendation • Personalised travel plan for the tourist • Tourist interests are matched against tourism object properties • If the tourist likes art, he/she will likely get recommendation about art-related objects. • Every object can have several different types (art, museum, eating place, rock concert etc.) • Every type has confidence score: • Object A is an eating place with confidence 80% • Object B is an art place with confidence 50% • User interest have also confidence or likeliness scores: • User likes museums with the score 80% • User likes eating places with the score 50%

4. Local recommender for Estonia • Local partner with data: • http://visitestonia.com/en/travel-planner • Cooperation with local data provider • Our system enriches partner's data from the Internet (popularity, user generated content etc.)

5. Recommendations for the whole world: • http://sightsmap.com/ • Data gathered and merged from different user generated content providers • Plan all your trips on the same portal • Recommendations are based on the user generated content - what others have liked • 30 000 daily visitors when first prototype was launched

6. Visit Estonia User profile Suggestionengine Partner database Personaltravel plan Tourismobjectinformation Popularity information Internet

7. Sightsmap User profile Suggestionengine Personaltravel plan Internet ... Data source (Panoramio) Data source (Foursquare)

8. Sightsmap • Heatmap based on Panoramio pictures • area with more pictures is more popular • location with a lot of pictures is probably a sightseeing object (something people take pictures of) • Picture titles are used to detect the name and the type of a location • common part of titles in one area indicates a name • existence of category words (like castle, restaurant, monument etc.) indicate the type of the object

9. Sightsmap • Sources used and merged in Sightsmap application: • Panoramio (picture density for heatmap) • Wikipedia (object names and types from wikipedia abstract) • foursquare (titles, popularity, types) • geonames (titles, types) • Wikitravel, Tripadvisor, Google Places as additional information links for objects

10. Challenges • Often pictures are taken from far, not on the spot (mountains, high buildings) • Multi language in (picture) titles • Currently, mostly outside sightseeing objects are presented (few restaurants, museums etc.) • Opening times • Termsand restrictions from data providers • Data amount and quality

11. Sightsplannernumbers • 50MPanoramio pictures • by 2M users • 800 000 geo-tagged Wikipedia articles • 8M Geonames places • 1.5M foursquare objects • 2Mhotspots • about 250 000 have connected Wikipedia article • about 550 000 have title (from Wikipedia or Foursquare) • about 30 000 unique visitors daily right after opening

12. Future work • Working on mobile application • Additional sources: • Flickr (additional pictures) • Facebook (events, user comments) • Evaluation: • Feedback from real tourists • Local planners for different cities/countries around Europe

13. Grazie! Questions? http://www.Sightsmap.com email: ago.luberg@eliko.ee