The newly redesigned TripAdvisor.com emphasizes traveler photos throughout the site, but not all of these photos make the best first impression. 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 talk will discuss our approach for gathering training data, developing a model, and scaling it up to 150+ million photos and 7+ million places of interest. Technologies discussed: Keras, TensorFlow, PySpark, Python multiprocessing, siamese networks, and to a lesser degree, S3, Hadoop/Hive/HDFS, and Kubernetes.
Greg Amis is a Principal Software Engineer on the Machine Learning team at TripAdvisor, where we tend to focus on very pragmatic projects-- ML that will quickly and directly improve our business. He’s been at TripAdvisor for over 3.5 years, working on machine vision, text processing (e.g., catching inappropriate content), and metadata processing (e.g., catching fraudulent reviews). Prior to TripAdvisor, he worked on government contracts, doing everything from adaptive radar jamming to forecasting Navy personnel needs. Greg has a PhD from Boston University in Cognitive and Neural Systems, studying a type of neural network called Adaptive Resonance Theory and its application to semi-supervised learning and remote sensing.