If you’ve redeemed points at the Chase Ultimate Rewards web site or booked business travel through American Express’ AXIOM service, you’ve already had some experience with Rearden Commerce’s Deem e-commerce services platform. In the not-too-distant future, such marketplace experiences may be even further informed by semantic technology to add even greater personalization.
VP of analytics Steve Bernstein’s duties include leading traditional quantitative analysis and predictive modeling – the kind of work that last week resulted in the company providing an analysis of ten years of big data on domestic flight performance to discover everything from the best day of the year to fly (Oct. 3, thanks to only7 in 1,000 arrivals or departures being late, cancelled or diverted to the worst arrival performance for a major airport (EWR, in Newark N.J.). But he spends the rest of his time working with other parts of his team to structure unstructured data with the help of semantics and natural language processing technology so that it can serve as input for predictive modeling purposes.
Today, structured information for traveler services that use the Deem platform come from third-party feeds, such as hotel information from providers like Orbitz that consist of a couple of hundred pieces of information on over 100,000 hotels. That’s helpful for pinpointing users to guest accommodations at their preferred location or price, for instance, but not so much for helping them book a quiet hotel room. That’s where his team’s work crawling and capturing over 5 million user-generated hotel reviews, loaded up into a Hadoop file system, can come into play to better serve users’ needs.