As a leading travel marketplace, Skyscanner is serving a daily load of up to a dozen billion flight itineraries to its users across the globe. The distribution of travel quotes at such a scale requires caching mechanisms optimized for minimizing the load on the partners (airlines and travel agencies) and maximizing the relevance and comprehensiveness of the itineraries to the travelers. This talk is focused on using data mining approaches for optimizing dynamic content distribution at scale and Skyscanner's efforts in this direction.

1. OptimizingContent
Caching atSkyscanner
@karamshuk

2. FlightSearch

3. Where
FlightQuotes
Come From
Airlines
Travel Agents
OnlineTravel
Agents
Global
Distribution
Systems
End User
– Each user search triggers dozen requests to partners resulting in a
total of 7B/day quotes
– Repeated requests with 85% probability return same price

4. Caching
Quotes
Airlines
Travel Agents
OnlineTravel
Agents
Global
Distribution
Systems
End User
Strong case for caching quotes:
– reduced load on partners
– faster results to end users
Quote Cache

5. Problem
withCaching
Prices are changing dynamically, so, caching may introduce inaccuracies
Bookings drop significantly even if the prices are slightly inaccurate

6. Caching
Trade-off Accuracy of Quotes
Load on Partners
and ResponseTime

7. Caching
Trade-off Accuracy of Quotes
Load on Partners
and ResponseTime
Optimal trade-off: Update prices only/always when they change

8. PriceVolatility
Not Easy
From: To:Moscow Barcelona

9. PriceVolatility
Not Easy
From: To:Moscow Bangkok

10. Predicting
PriceVolatility
1. Approach N1: constant cache expiry times
– simple to implement
– does not accurately model price volatility
2. Approach N2: emulate pricing models of each individual partner
– pricing models of some airlines are incredibly complex
3. Approach N3: machine learning approach
– best trade-off between simplicity and accuracy

11. Machine
Learning
Approach
1. Supervised learning: Leverage on the archive of 7B/day quotes
2. Encapsulate wide range of factors: Learn patterns specific for
individual airlines, routes, booking time and horizon, popularity, etc.
3. Automatically adjust to different pricing models

12. Preliminary
Results

13. Challenges
1. Purifying labels; dealing with unbalanced classes
2. Limitation of supervised (imitation) learning
3. Implementing scalable solution

14. Get in touch:
@karamshuk
dima.karamshuk@skyscanner.net