Improving TripAdvisor Photo Selection With Deep Learning

Gregory Amis posted November 18, 2017
The newly redesigned TripAdvisor.com emphasizes traveler photos throughout the site, but not all of these photos are useful in every situation. Deep Learning networks provide an excellent opportunity for us to improve our users’ experience by highlighting the most attractive and useful photos for varying presentation contexts. This post will discuss our approach for gathering training data, developing a model, and scaling it up to over 110 million photos and…
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Using Apache Spark for Massively Parallel NLP

Jeff Palmucci posted July 17, 2015
Here at TripAdvisor we have a lot of reviews, several hundred million according to the last announcement. I work with machine learning, and one thing we love in machine learning is putting lots of data to use. I've been working on an interesting problem lately and I'd like to tell you about it. In this post, I'll set up the problem and the underlying technology that makes it possible. I'll get…
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Which of TripAdvisor’s reviews are actually helpful?

Gregory Amis posted July 10, 2015
At TripAdvisor, we use machine learning to assess whether a user’s review is substantive and helpful to other users. This article describes our motivations, technology, and results. Problem description TripAdvisor members submit nearly one million reviews every week. We want to publish only the reviews that are helpful to other travelers, but our moderation team can’t possibly read every submitted review. If we can programmatically score a review’s helpfulness, we…
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Ordering Hotels on TripAdvisor as a Minimum Feedback Arc Set problem

Craig Schmidt posted June 26, 2015
Hello from the TripAdvisor Machine Learning Group This is the first in a series of blog posts from the Machine Learning Group at TripAdvisor. We get to work on lots of fun, interesting problems, and we thought you might like to hear about them. What order should we show our hotels? Take a Hotels page for a city on TripAdvisor, like this one for Boston.   How should we sort…
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