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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