In many different web services, machine learning is being used for recommendation systems that help users tackle information overload: there are simply too many movies, songs, and books for users to usefully browse through. Without such tools, some services are rapidly falling behind and losing customers.
Travel is a little bit different, as the world does not have millions of cities, but finding new, interesting places to travel to is still a challenge. Years ago, Skyscanner
started it’s ‘everywhere’ search which allows users to find the cheapest places possible that they could travel to, leading to research showing that price is one of many factors that make a place attractive and interesting.
Neal Lathia, Senior Data Scientist at Skyscanner, will join us at the Machine Intelligence Summit
in Amsterdam, to share how the company bootstrapped a destination recommender system using rich implicit data generated by millions of users, along with simple algorithmic approaches, and experiments that gauge how localised and personalised recommendation affects user engagement. I spoke to Neal ahead of the event to learn more.
Please tell us more about your work at Skyscanner.
As a Senior Data Scientist, my focus is on designing and building machine learning features for Skyscanner's mobile app. Since joining, just under a year ago, the projects I've been working on have related to recommendation and search result ranking. However, the app creates a very rich ecosystem of data, and we have already identified a number of other opportunities ahead.
What do you feel are the leading factors enabling recent advancements in machine learning for recommendation systems?
Many of the near state-of-the-art algorithms for recommendation systems have been open sourced- which is always welcome news! The research field has also always been driven by open data challenges. Most importantly, the research community has always taken a multidisciplinary approach - not all recommender system challenges need to be solved with machine learning.
Which industries have the biggest potential to be impacted by advancements in recommendation systems?
As someone who has a background in recommender systems, it is difficult for me to try to envisage any industry without the lens of recommendation potential. There are so many facets of life where personalised information could be useful - from healthcare to travel and beyond.
What developments can we expect to see in machine intelligence in the travel industry in the next 5 years?
Many of the best known travel sites online have a distinct focus on price - helping users find the cheapest flight, hotel, or car (Skyscanner is no exception to this!). As these services gain greater smartphone traction, and data (e.g., flight statuses and prices) becomes available in real-time, the travel industry is going to become a ripe domain for machine intelligence applications.
Outside of your own field, what area of machine learning do you will see the most progress in the next 5 years?
There is no doubt that recent advances in neural networks have lead to wonderful results in the areas of reinforcement learning and machine vision - I expect that progress to continue to accelerate. I'm looking forward to interesting products that may arise from these areas of research.
Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.
|Neal Lathia will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more. |
Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here.
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