Hotels.com uplifts big data to get closer to customers
Hotels.com has upgraded its data science operations to Apache Spark running in the cloud to help it become more “algorithmic” in the way it presents accommodation options and features to customers.
Chief data science officer and vice president Matt Fryer told the Spark Summit in San Francisco that the online booking site – which is owned by Expedia – wanted to get better at tailoring image content and hotel listings to individual users.
That required a major uplift of the underlying technology used to perform data science and big data analytics.
“About 18 months ago there was data piling up everywhere,” Fryer said.
“We had an on-premises Hadoop cluster in our data centre, and we were using SQL and SAS to do our data science, algorithms and stats.
“In 18 months we’ve completely changed that view. We’ve gone 100 percent into the cloud, particularly AWS. We’ve reinvigorated our applications, and in particular we’ve moved to using [Apache] Spark for 100 percent of our workflows. That’s been a huge boost both for our business and our customers.”
The company is now running two major data science platforms.
Fryer said Hotels.com makes “extensive use” of Databricks, a cloud-based big data platform powered by Spark. It provides a single place to manage clusters and explore data, among other functions.
“We also have a back-up [platform] called Maestro which picks up some of the elements that we can’t do in Databricks right now,” Fryer said.
The internally-developed Maestro also uses Spark and sits on AWS. It harnesses a number of other tools and technologies including TensorFlow, RStudio, Jupyter and interactive build tool SBT.
Fryer said Hotels.com is also experimenting with a proof-of-concept platform running Apache Beam and Spark as well as TensorFlow on top of the Google Cloud Platform.
In addition, it has been experimenting with GPUs and TensorFlow to reduce the time it takes to train deep learning models to recognise certain things, particularly in image data.
However, Fryer said the GPU work “has been really hard” and enormous effort had been expended to extract speed improvements that justified the expense of using them.
One of the areas of Hotels.com where machine learning is being put to use is around how photos are captioned, presented and ordered.
“After price, location and what’s in the hotel, the photo images are the next most important thing to customers,” Fryer said.
