• AI Projects with practical Experience

Tourism guidedance by AI - the best recommendation in town

Tourism guidedance by AI - the best recommendation in town

Project Profile

Which tourist attraction can be recommended to a tourist as the next destination? We were on the trail of this question with the client. The next destination is certainly influenced by the previous POIs and may be reflected in the origin and many other parameters. We solved the matter with a recommendation engine that in any case creates a correct derivation as the next target from the previously learned targets per market. So we land the best recommender on the market which is absolutely independent from COVID!

To try out: https://ai.calista.at/ViennaInsights

At a glance - essential project data

DurationFrom 4/1/2020 to 7/30/2020 with about 4 months of full engagement
Data and ToolsMarket - Tourism
 • Mobile data
 • Flight data VIE / BTL
 • Weather
 • Global vacation calendar
IntegrationWeb API and Website for Demonstration Purposes
AI Methods • Deep - Learning
 • LSTM Model with Embeddings

Engagement Use-Case

Recommendation for an optimized visit itinerary (sequence of places or buildings) for tourists usable in CityApps embedding

Client motivation / Solution aims

  • Incentivize wayfinding for guides

  • tourists and tour groups

  • Design a maximum efficient offer for tourism programs individualized for all markets

  • To be able to recommend the ideal individualized itinerary for each touristic market in a city (Mobile Apps)

AI Approach

AI key technology used in our solutionNo comparable model known. Requirements were completely implemented in the neural network.
Solution ApproachMultiple DeepLearning models (DNN) with embedding of origin countries and POIs as well as 3-stage memory.
Project ApproachSimply agile
Project TypeProof-Of-Concept (POC)
ML Integration and ML Operations • Operation Integration API
 • Visualization: Website https://ai.calista.at/ViennaInsights

Insights and Details

Small insight into the market behavior of tourists at POIs learned through embeddings.

The train through the city - redefined!

Which markets move through Vienna - which markets should be worked on together, because they have common ways - here the Miracle is solved