Bootstrapping a Destination Recommendation Engine@neal_lathia

What is Skyscanner

(Some of the) Product Machine Learning Problems

Price AccuracyEnsuring that what you see is what you’ll get

SearchFinding the best itinerary for your needs

RecommendationInspiring you to travel to new places

Ad relevanceConnecting partners with the right travellers

ConversationsGo and try our Facebook bot J

AlertingKeeping you informed, finding the best time to buy

Can we do better?

Historical price focusPrice is only one feature that could make a destination attractive.

Sparse user dataTravel is (relatively) low frequency. Many new, anonymous users –cold start problem in recommendation.

Destinations are relativeLondon from Edinburgh is not the same as London from New York.

…with specific challenges

No collaborative filtering (yet)Traditional collaborative filtering algorithms are not suitable for the data that we have.

No manual interventionMany 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).

Key insight

Pipeline Overview

Write the code: The architecture behind Skyscanner’s recommended destinations (by @AndreBarbosa88)https://medium.com/towards-data-science/write-the-code-f6d58c728df0

InitialStructure

Many ways to define three key concepts

PopularWhere do people want to (always, recently) go?

“Localised”What is in higher demand where you are? Destination-frequency, inverse global frequency.

TrendingTemporal shifts in search behaviours to captureseasonality, events, demand.

Experiments

“Design like you’re right, test like you’re wrong” by @MCFRLhttp://codevoyagers.com/2016/03/16/design-like-youre-right-test-like-youre-wrong/

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Conclusions

thanks: Vespa Squad in London, Data Science team!