This document discusses using machine learning to provide personalized experiences on Skyscanner. It describes three examples: 1) Destination recommendation based on unsupervised learning of popular, local, and trending destinations. 2) Itinerary recommendation framed as a supervised learning ranking problem. 3) Contextual support using multi-armed bandits to learn which search tools and messages work best in different contexts without imposing new burdens on users. It also discusses challenges like sparse travel data and the complexity of different search combinations and new ideas. Lessons learned include references on machine learning for product managers and the state of the field.

1. AI & Personalised Experiences
@neal_lathia, Senior Data Scientist, Skyscanner
November 29, 2017

4. A variety of machine learning problems
● Price accuracy & caching
● Flight itinerary search
● Destination inspiration & recommendation
● Advertisement relevance ranking
● Growth forecasting & customer value
● Conversations
● ...many more.

6. Personalised Experiences

7. Tailoring the product to specific customers

8. 3 Examples
1. Destination Recommendation
2. Itinerary Recommendation
3. Contextual Support
From current experiments

9. 1. Destination Recommendation

12. Can we do better?
● Historical focus on cheapness: price is only 1 thing that matters
● Sparse user data - travel is infrequent
● Destinations are relative - London from Edinburgh is not the same as
London from New York.
● … without imposing any new burden on our app users?

13. Recommending destinations based on unsupervised learning
Popular, Localised, Trending

14. Recommending destinations

15. 2. Itinerary Recommendation

16. Itineraries as a ranking problem

17. Can we do better?
● Historical focus on cheapness: price is only 1 thing that matters
● Sparse user data - travel is infrequent
● Itineraries are complicated to trade-off against one another
● … without imposing any new burden on our app users?

18. Itinerary ranking as a supervised learning problem

19. Recommendations as overlaid results

20. 3. Contextual Support

21. From search results into result controls

22. From search results into result controls

23. Can we do better?
● We know that some search tools are helpful in some situations, and less
helpful in other situations
● Thousands of different search combinations - how can we manage the
complexity of figuring out when a specific search tool is helpful?
● Many ideas for new search tools, tips, and messages - how can we
manage the complexity of adding new types of results without any
historical data?
● … without imposing any new burden on our app users?

24. Multi-armed bandits to learn what support works in what contexts
Multi-armed bandits work by automating the process of
exploring various layouts -- and then being able to exploit the
layout that has worked best across each specific context.

25. Multi-armed bandits to learn what support works in what contexts

26. Lessons Learned

27. Machine Learning for Product Managers. (Medium)

28. The AI Hierarchy of Needs. M. Rogati.

29. Rules of Machine Learning: Best Practices for ML Engineering. M. Zinkevich.

30. The State of Data Science and Machine Learning 2017. (Kaggle)

31. AI & Personalised Experiences
@neal_lathia