We love to feel like a local when we travel. And we believe that with Triposo on your phone you can feel like a local. You can wander around without the fear of being lost because our offline maps will always get you home, you have suggestions that will tell you about the best places in the area and we make sure you will know about local events happening.
During the Scotland Jamboree I set out to find out what factors determine if a local is likely to suggest a restaurant or a bar to a travelers.
I then created formula with these factors. By minimizing the sum of squared differences I could optimize the algorithm to predict the scores in the training set.
The following graph shows how different factors impact how likely locals are to recommend a place:
* Facebook statistics seemed to be too noisy to use, mostly because the activity of the page owner has a stronger influence. In a next round I would like to compare Facebook popularity with Twitter popularity to see if a difference exists between owners that promote their place on Facebook rather than on Twitter and if this correlates with certain types of places.
* I ended up adding some corrections to the algorithm because my initial set did not have any real outliers: they all had perfect data. Which means they all had scores for all of the factors above. When you then use the algorithm on 500K points of interest you see that missing matches influences scores to much.
* The training set was probably too small for scientific purposes or even for producing a algorithm we can use tomorrow in our apps. But all in all this shows real promise. It does bring up really good, slightly untouristy places in all the destinations we tried!
