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Artificial Intelligence for Travel - MyLittleAdventure / Welcome City Lab Demo Day

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An overview of how MyLittleAdventure use Artificial Intelligence technologies to help travellers to choose their best memories : Smart inspiration engine, easy comparison of things to do in destination and many more

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Artificial Intelligence for Travel - MyLittleAdventure / Welcome City Lab Demo Day

  1. 1. B2B Travel technology
  2. 2. MyLittleAdventure A personalization technology provider that inspires travellers with things to do at destination 2 « Tomorrow’s travelers will want to travel the world in just one way – their own way. » A personalized travel future - Amadeus IT Group
  3. 3. 3 A highly fragmented market
  4. 4. 4 Hundreds of websites
  5. 5. MyLittleAdventure Solution & Tech insights 5
  6. 6. Intelligent engine inside 6 First class components and intelligent Technology X The right product to the right person at the right moment ! Data Collection Bookable & informative " Segmentation Machine Learning # Personalization Collaborative Filtering X 31 2
  7. 7. Architecture overview 7 # Personalization Collaborative Filtering ! Data Collection Bookable & informative $ % & ' ( ) Social Feeds " Segmentation Machine Learning * Reviews ⋆ Ratings , Product Feeds | Statistics SupplierLayer BusinessLayer . Travel data / Web Content 0 Booking 1 Profile context 2 Accounting core Product channels 3 4Widgets 5API / Webservices 6 IoT Feeds  Big data First class components and intelligent Technology … … 1 2 3 8Ads & Banners
  8. 8. Clustering 1. Detect similar products Unsupervised problem Not possible for a human being Clustering strategy Too many products (270k) & updates (everyday) 8 2. ML Algorithms & tools Mean shift, K-Means, Spectral Co-Clustering, Hierarchical clustering SVD (LSA), Embeddings NLTK, Scikit learn
  9. 9. Clustering 9 25 similar products
  10. 10. Features scoring 1. Detect & score product features Classification : not reliable and not uniform across suppliers Score each products for each available categories Generic technology New kind of search engine : Preferences first, not just filters 10 2. ML Algorithms & tools Random forest, Gradient Boosting, Neural networks, Ridge regression, Lasso, Naive Bayes SVD (LSA), Embeddings NLTK, Scikit learn, Dataiku
  11. 11. Features scoring 11
  12. 12. Hyper-Personalization 1. With some Traveler information Benefit from content and collaborative filtering 12 Foursquare Swarm TripAdvisor Places Facebook Yelp! Twitter 3. Without any travel context Tap billions of signals from social network content Intelligent engine to calculate traveler trends around the world 2. With some Travel context Use of implicit features : duration, customer segment, weather,…
  13. 13. And some others… 1. Language detection Ridge regression, Lasso, Naive Bayes 13 2. Social places merge Random forest 3. Product image similarity Neural networks (CNN)
  14. 14. Traveler advantages 14 + Gain time + Personalization + New activity ideas + More happiness + More confidence - less risk + Easy comparison (Price - Activities - Options)
  15. 15. + Increase customer experience and loyalty + New source of margin (Cross-Sell) + Enrich customer global services + One sole agreement + Essential for Concierge + No cost of development nor integration Travel Stakeholder advantages 15
  16. 16. 16 Business case Customer satisfaction More loyalty More revenue Smart Recommandations Travel stakeholders
  17. 17. Thank you CONTACT Mr Valéry BERNARD - CEO & Founder valery.bernard@mylittleadventure.com 06.17.21.52.81
  18. 18. MyLittleAdventure Integration examples 18
  19. 19. API / Webservices 19 Json / Xml API verbs
  20. 20. Widgets - Web 20
  21. 21. Widgets - Mobile 21 A clear, simple and seamless customer experience
  22. 22. MyLittleAdventure Catalog 22
  23. 23. 23 MyLittleAdventure catalog 270 000 Products - 175 Countries - 1617 Cities 15 Languages
  24. 24. 24 European products
  25. 25. Technical stack https://stackshare.io/mylittleadventure/mylittleadventure 25

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