AI & Personalised Experiences@neal_lathia, Senior Data Scientist, Skyscanner

November 29, 2017

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.

Personalised Experiences

Tailoring the product to specific customers

3 Examples 1. Destination Recommendation2. Itinerary Recommendation3. Contextual Support

From current experiments

1. Destination Recommendation

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?

Recommending destinations based on unsupervised learning

Popular, Localised, Trending

Recommending destinations

2. Itinerary Recommendation

Itineraries as a ranking problem

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?

Itinerary ranking as a supervised learning problem

Recommendations as overlaid results

3. Contextual Support

From search results into result controls

From search results into result controls

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?

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.

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

Lessons Learned

Machine Learning for Product Managers. (Medium)

The AI Hierarchy of Needs. M. Rogati.

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

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

AI & Personalised Experiences@neal_lathia