Thomas Reiter
100 million days in Vienna: A story of APIs & AI in tourism.
#1about 3 minutes
Introducing the "Make My Day" tourism prototype
The application's tech stack is introduced alongside key vocabulary from the tourism industry to set the context for the project.
#2about 9 minutes
A live demonstration of the itinerary planning app
A walkthrough of the "Make My Day" web application shows how a user answers a quiz to generate a personalized, actionable daily plan for Vienna.
#3about 5 minutes
Understanding the system architecture with the C4 model
The C4 model is used to explain the high-level architecture, showing how the application interacts with the central data hub and various external services.
#4about 7 minutes
Designing APIs for data aggregation and UI plugins
An exploration of specific APIs, including an opinionated location aggregation service and an oEmbed-based service for embedding UI components like maps.
#5about 6 minutes
How the "dress a skeleton" planning algorithm works
The core logic for creating itineraries is a 10-step procedural algorithm that incrementally fills a pre-defined day structure with matching points of interest.
#6about 4 minutes
Using machine learning to classify touristic data
Logistic regression is used to classify places of interest based on atmosphere and weather suitability, highlighting the challenges of human bias in data labeling.
#7about 4 minutes
Building a data aggregation and enrichment pipeline
An ETL-like pipeline retrieves structured and unstructured data, uses NLP to link it to points of interest, and aggregates it into a knowledge graph using schema.org.
#8about 4 minutes
Key learnings and evolving the prototype into a framework
The project's key takeaways, including the limitations of NLP in Node.js, led to evolving the prototype into a reusable framework for other tourism destinations.
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