Amadeus S.a.s.

Rodrigo Acuna Agost

David Renaudie

Alejandro Ricardo Mottini D'Oliveira

Maria Zuluaga

Alix Lheritier

Methods of evaluating and deploying machine learning models for anomaly detection of a monitored system and related systems. Candidate machine learning algorithms are configured for anomaly detection of the monitored system. For each combination of candidate machine learning algorithm with type of anomalous activity, training and cross-validation sets are drawn from a benchmarking dataset. Using each of the training and cross-validation sets, a machine-learning model is trained and validated using the cross-validation set with average precision as a performance metric. A mean average precision value is then computed across these average precision performance metrics. A ranking value is computed for each candidate machine learning algorithm, and a machine learning algorithm is selected from the candidate machine learning algorithms based upon the computed ranking values. The selected machine learning model is deployed to a monitoring system that executes the deployed machine learning model to detect anomalies of the monitored system.

System and method for evaluating and deploying unsupervised or semi-supervised machine learning models

Methods and computing apparatus for real-time online traveler segmentation. A machine learning classifier may be trained using computed feature vectors and associated tags corresponding with records in a training set. A machine learning classifier receives a feature vector comprising values of the plurality of features corresponding with an unidentified user in an online context. The machine learning classifier may determine an estimate of whether the unidentified user is a member or a non-member of a predetermined traveler category.

Systems and methods for real-time online traveler segmentation using machine learning

Methods and computing apparatus for retrieving records relating to content placement events and records relating to user interaction events. A set of enriched training feature vectors is computed from raw feature values, and used with interaction event tags to train a machine learning model. Information is received relating to an online content placement slot and information is received relating to a user to whom content within the online content placement slot will be displayed. An enriched estimation feature vector is computed based upon a content item selected for placement within the online content placement slot, the information relating to the user, and the information relating to the online content placement slot. A machine learning model is executed to determine an estimate of likelihood of the user interacting with the selected content item, based upon the enriched estimation feature vector.

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Golfe Juan, France

Rodrigo Acuna Agost