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Bootstrapping a Destination Recommendation Engine

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Slides from my ACM Recommender Systems 2017 Industry-track talk. Como, Italy.

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Bootstrapping a Destination Recommendation Engine

  1. 1. Bootstrappinga Destination Recommendation Engine @neal_lathia
  2. 2. What is Skyscanner
  3. 3. (Some of the) Product Machine Learning Problems Price Accuracy Ensuring that what you see is what you’ll get Search Finding the best itinerary for your needs Recommendation Inspiring you to travel to new places Ad relevance Connecting partners with the right travellers Conversations Go and try our Facebook bot J Alerting Keeping you informed, finding the best time to buy
  4. 4. Can we do better? Historical price focus Price is only one feature that could make a destination attractive. Sparse user data Travel is (relatively) low frequency. Many new, anonymous users – cold start problem in recommendation. Destinations are relative London from Edinburgh is not the same as London from NewYork.
  5. 5. …with specific challenges No collaborative filtering (yet) Traditional collaborative filtering algorithms are not suitable for the data that we have. No manual intervention Many approaches that tackle cold-start require manual intervention from users: profiles, surveys, tags, preferences. No offline evaluation (yet) Without data, we have no robust approaches to estimating the accuracy of recommendations offline (e.g., RMSE).
  6. 6. Key insight
  7. 7. PipelineOverview
  8. 8. Write the code: The architecture behind Skyscanner’s recommended destinations (by @AndreBarbosa88) https://medium.com/towards-data-science/write-the-code- f6d58c728df0 Initial Structure
  9. 9. Many ways to define three key concepts Popular Where do people want to (always, recently) go? “Localised” What is in higher demand where you are? Destination-frequency, inverse global frequency. Trending Temporal shifts in search behaviours to capture seasonality, events, demand.
  10. 10. Experiments “Design like you’re right, test like you’re wrong” by @MCFRL http://codevoyagers.com/2016/03/16/design-like-youre-right-test-like-youre- wrong/
  11. 11. ✅ ❌
  12. 12.
  13. 13. Conclusions
  14. 14. thanks:Vespa Squad in London, Data Science team!

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